# Cai dat cac thu vien can thiet
There were 23 warnings (use warnings() to see them)
# install.packages("moments")
# install.packages("dplyr")
# install.packages("readxl")
# install.packages("ggplot2)
# install.packages("hrbrthemes")
# Load cac thu vien can thiet
library(moments) # Dung de thuc hien cac fucntion Statistics
library(dplyr) # Dung de lam viec voi cac Dataframe
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library(readxl) # Dung de doc file Excel
library(xlsx)
library(ggplot2) # Dung de ve do thi
library(gridExtra) # Dung de dan layout cho cac plot
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combine
library(grid)
library(hrbrthemes) # Dung de load theme cho cac plot
NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(ggpubr)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
# Tao dataframe Giua ky tu trich xuat file Excel - sheet "GK"
dataGK <- read_excel("192_CO1007.xlsx", sheet = "GK", skip = 4)
dataGK[!complete.cases(dataGK),]
# Tao dataframe Cuoi ky tu trich xuat file Excel - sheet "CK"
dataCK <- read_excel("192_CO1007.xlsx", sheet = "CK", skip = 4)
# Hien thi dataframe Giua ky
dataGK
# Hien thi dataframe Cuoi ky
dataCK
# Ket qua cham thi Giua ky
KetQuaGK <- dataGK[, c("No", "MANH", "TO", "MADE", "1":"25")]
KetQuaGK
# Bai thi cua Sinh vien Giua ky
# Boi vi trong BaiThiGK co nhung cau Sinh vien bo trong (value = NA), nen chung ta se khong the goi nhu tren
BaiThiGK <- dataGK[, c(1, 2, 3, 29:54)]
BaiThiGK
# Ket qua cham thi Cuoi ky
KetQuaCK <- dataCK[, c("No", "MANH", "TO", "MADE", "1":"29")]
KetQuaCK
# Bai thi cua Sinh vien Cuoi ky
# Tuong tu nhu BaiThiGK
BaiThiCK <- dataCK[, c(1, 2, 3, 33:62)]
BaiThiCK
# Dung dplyr to cong So cau dung va So cau sai cua tung Sinh vien
# Tao Bang So dap an dung va sai cua sinh vien Giua ky
SoCauDungVaSaiCuaSVGK <- KetQuaGK %>% rowwise() %>%
mutate(
SoCauDung = sum(c_across("1":"25")),
SoCauSai = 25 - sum(c_across("1":"25"))
)
# Tao Bang So dap an dung va sai cua sinh vien Cuoi ky
SoCauDungVaSaiCuaSVCK <- KetQuaCK %>% rowwise() %>%
mutate(
SoCauDung = sum(c_across("1":"29")),
SoCauSai = 29 - sum(c_across("1":"29"))
)
# In ra Tong so cau Dung trong ky thi Giua ky cua sinh vien trong tap mau
TongSoCauDungCuaSVGK = SoCauDungVaSaiCuaSVGK[, c("No", "MANH", "TO", "MADE", "SoCauDung")]
TongSoCauDungCuaSVGK
# In ra Tong so cau Dung trong ky thi Cuoi ky cua sinh vien trong tap mau
TongSoCauDungCuaSVCK = SoCauDungVaSaiCuaSVCK[, c("No", "MANH", "TO", "MADE", "SoCauDung")]
TongSoCauDungCuaSVCK
# In ra Tong so cau Sai trong ky thi Giua ky cua sinh vien trong tap mau
TongSoCauSaiCuaSVGK = SoCauDungVaSaiCuaSVGK[, c("No", "MANH", "TO", "MADE", "SoCauSai")]
TongSoCauSaiCuaSVGK
# In ra Tong so cau Sai trong ky thi Cuoi ky cua sinh vien trong tap mau
TongSoCauSaiCuaSVCK = SoCauDungVaSaiCuaSVCK[, c("No", "MANH", "TO", "MADE", "SoCauSai")]
TongSoCauSaiCuaSVCK
# Xac dinh cau dung nhieu nhat va nho nhat trong tap mau Giua ky
# Tinh ra Danh sach (List) so lan Dung cua moi cau Giua ky
SoCauDungGK <- colSums(dataGK[,c(4:28)])
CauDungNhieuNhatGK <- subset(SoCauDungGK, SoCauDungGK == max(SoCauDungGK))
CauDungItNhatGK <- subset(SoCauDungGK,SoCauDungGK == min(SoCauDungGK))
sprintf("Cau %s la cau duoc chon dung nhieu nhat Giua ky voi %s lan chon dung", names(CauDungNhieuNhatGK), CauDungNhieuNhatGK)
[1] "Cau 2 la cau duoc chon dung nhieu nhat Giua ky voi 350 lan chon dung"
sprintf("Cau %s la cau duoc chon dung it nhat Giua ky chi voi %s lan chon dung", names(CauDungItNhatGK), CauDungItNhatGK)
[1] "Cau 6 la cau duoc chon dung it nhat Giua ky chi voi 0 lan chon dung"
# Xac dinh cau dung nhieu nhat va nho nhat trong tap mau Cuoi ky
# Tinh ra Danh sach (List) so lan Dung cua moi cau Cuoi ky
SoCauDungCK <- colSums(dataCK[,c(4:32)])
CauDungNhieuNhatCK <- subset(SoCauDungCK,SoCauDungCK == max(SoCauDungCK))
CauDungItNhatCK <- subset(SoCauDungCK,SoCauDungCK == min(SoCauDungCK))
sprintf("Cau %s la cau duoc chon dung nhieu nhat Cuoi ky voi %s lan chon dung", names(CauDungNhieuNhatCK), CauDungNhieuNhatCK)
[1] "Cau 20 la cau duoc chon dung nhieu nhat Cuoi ky voi 317 lan chon dung"
sprintf("Cau %s la cau duoc chon dung it nhat Cuoi ky chi voi %s lan chon dung", names(CauDungItNhatCK), CauDungItNhatCK)
[1] "Cau 12 la cau duoc chon dung it nhat Cuoi ky chi voi 157 lan chon dung"
# Tinh So lan chon dung moi cau theo ma de Giua ky
# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1921
SoCauDungGK1921 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonDungGK1921 <- table(SoCauDungGK1921$SoCauDung)
dDungGK1921 <- data.frame(SoLanChonDungGK1921)
colnames(dDungGK1921) <- c("SoCauDung", "SoSinhVien")
pDungGK1921 <- ggplot(dDungGK1921, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="steelblue") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1922
SoCauDungGK1922 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungGK1922 <- table(SoCauDungGK1922$SoCauDung)
dDungGK1922 <- data.frame(SoLanChonDungGK1922)
colnames(dDungGK1922) <- c("SoCauDung", "SoSinhVien")
pDungGK1922 <- ggplot(dDungGK1922, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="darkgoldenrod1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1923
SoCauDungGK1923 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungGK1923 <- table(SoCauDungGK1923$SoCauDung)
dDungGK1923 <- data.frame(SoLanChonDungGK1923)
colnames(dDungGK1923) <- c("SoCauDung", "SoSinhVien")
pDungGK1923 <- ggplot(dDungGK1923, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="darkseagreen") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1924
SoCauDungGK1924 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonDungGK1924 <- table(SoCauDungGK1924$SoCauDung)
dDungGK1924 <- data.frame(SoLanChonDungGK1924)
colnames(dDungGK1924) <- c("SoCauDung", "SoSinhVien")
pDungGK1924 <- ggplot(dDungGK1924, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Hien thi 4 bieu do
g <- grid.arrange(
arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
nrow = 2,
bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Giữa kỳ"
)

# Luu hinh anh vao thu muc hinh
ggsave("hinh/5241_tongsocaudung_gk.jpg", g)
Saving 10 x 10 in image
# Tinh So lan chon dung moi cau theo ma de Cuoi ky
# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1921
SoCauDungCK1921 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonDungCK1921 <- table(SoCauDungCK1921$SoCauDung)
dDungCK1921 <- data.frame(SoLanChonDungCK1921)
colnames(dDungCK1921) <- c("SoCauDung", "SoSinhVien")
pDungCK1921 <- ggplot(dDungCK1921, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1922
SoCauDungCK1922 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungCK1922 <- table(SoCauDungCK1922$SoCauDung)
dDungCK1922 <- data.frame(SoLanChonDungCK1922)
colnames(dDungCK1922) <- c("SoCauDung", "SoSinhVien")
pDungCK1922 <- ggplot(dDungCK1922, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1923
SoCauDungCK1923 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungCK1923 <- table(SoCauDungCK1923$SoCauDung)
dDungCK1923 <- data.frame(SoLanChonDungCK1923)
colnames(dDungCK1923) <- c("SoCauDung", "SoSinhVien")
pDungCK1923 <- ggplot(dDungCK1923, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1924
SoCauDungCK1924 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonDungCK1924 <- table(SoCauDungCK1924$SoCauDung)
dDungCK1924 <- data.frame(SoLanChonDungCK1924)
colnames(dDungCK1924) <- c("SoCauDung", "SoSinhVien")
pDungCK1924 <- ggplot(dDungCK1924, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Hien thi 4 bieu do
g <- grid.arrange(
arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
nrow = 2,
bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Cuối kỳ"
)

# Luu hinh anh vao thu muc hinh
ggsave("hinh/5242_tongsocaudung_ck.jpg", g)
Saving 10 x 10 in image
# Tinh So lan chon sai moi cau theo ma de Giua ky
# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1921
SoCauSaiGK1921 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonSaiGK1921 <- table(SoCauSaiGK1921$SoCauSai)
dSaiGK1921 <- data.frame(SoLanChonSaiGK1921)
colnames(dSaiGK1921) <- c("SoCauSai", "SoSinhVien")
pSaiGK1921 <- ggplot(dSaiGK1921, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1922
SoCauSaiGK1922 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiGK1922 <- table(SoCauSaiGK1922$SoCauSai)
dSaiGK1922 <- data.frame(SoLanChonSaiGK1922)
colnames(dSaiGK1922) <- c("SoCauSai", "SoSinhVien")
pSaiGK1922 <- ggplot(dSaiGK1922, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1923
SoCauSaiGK1923 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiGK1923 <- table(SoCauSaiGK1923$SoCauSai)
dSaiGK1923 <- data.frame(SoLanChonSaiGK1923)
colnames(dSaiGK1923) <- c("SoCauSai", "SoSinhVien")
pSaiGK1923 <- ggplot(dSaiGK1923, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1924
SoCauSaiGK1924 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonSaiGK1924 <- table(SoCauSaiGK1924$SoCauSai)
dSaiGK1924 <- data.frame(SoLanChonSaiGK1924)
colnames(dSaiGK1924) <- c("SoCauSai", "SoSinhVien")
pSaiGK1924 <- ggplot(dSaiGK1924, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Hien thi 4 bieu do
g <- grid.arrange(
arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
nrow = 2,
bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Giữa kỳ"
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5243_tongsocausai_gk.jpg", g)
# Tinh So lan chon sai moi cau theo ma de Cuoi ky
# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1921
SoCauSaiCK1921 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonSaiCK1921 <- table(SoCauSaiCK1921$SoCauSai)
dSaiCK1921 <- data.frame(SoLanChonSaiCK1921)
colnames(dSaiCK1921) <- c("SoCauSai", "SoSinhVien")
pSaiCK1921 <- ggplot(dSaiCK1921, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1922
SoCauSaiCK1922 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiCK1922 <- table(SoCauSaiCK1922$SoCauSai)
dSaiCK1922 <- data.frame(SoLanChonSaiCK1922)
colnames(dSaiCK1922) <- c("SoCauSai", "SoSinhVien")
pSaiCK1922 <- ggplot(dSaiCK1922, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1923
SoCauSaiCK1923 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiCK1923 <- table(SoCauSaiCK1923$SoCauSai)
dSaiCK1923 <- data.frame(SoLanChonSaiCK1923)
colnames(dSaiCK1923) <- c("SoCauSai", "SoSinhVien")
pSaiCK1923 <- ggplot(dSaiCK1923, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1924
SoCauSaiCK1924 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonSaiCK1924 <- table(SoCauSaiCK1924$SoCauSai)
dSaiCK1924 <- data.frame(SoLanChonSaiCK1924)
colnames(dSaiCK1924) <- c("SoCauSai", "SoSinhVien")
pSaiCK1924 <- ggplot(dSaiCK1924, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)
# Hien thi 4 bieu do
g <- grid.arrange(
arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
nrow = 2,
bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Cuối kỳ"
)

# Luu hinh anh vao thu muc hinh
ggsave("hinh/5244_tongsocausai_ck.jpg", g)
Saving 10 x 10 in image
Phan III
# Tinh diem cua moi sinh vien trong ky thi Giua ky va Cuoi ky
dataGK$TongDiemGK = (round(rowSums(dataGK[,c(4:28)])/25*10,1))
dataCK$TongDiemCK = (round(rowSums(dataCK[,c(4:32)])/29*10,1))
# Tao ra 1 table moi chi gom 2 cot No & TongDiemGK/TongDiemCK
DiemCuaMoiSVGK<- select(dataGK,No,TongDiemGK)
DiemCuaMoiSVCK<- select(dataCK,No,TongDiemCK)
# Theme cho table
thm <- ttheme_minimal(
core=list(bg_params = list(fill = blues9[1:4], col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="orange", fontface=3L)))
# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
tableGrob(head(DiemCuaMoiSVGK, 10), theme = thm),
tableGrob(head(DiemCuaMoiSVCK, 10), theme = thm),
ncol=2
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5311_diem.jpg", g)
# Tao 1 bang moi bang cach join 2 bang DiemCuaMoiSVCK & DiemCuaMoiSVGK
DiemTongKet<-full_join(DiemCuaMoiSVCK,DiemCuaMoiSVGK)
Joining, by = "No"
DiemTongKet$TongKet = round(((rowSums(DiemTongKet[,c(2:2)])*0.6 +
rowSums(DiemTongKet[,c(3:3)])*0.4)),1)
DiemTongKet
# Theme cho table
thm <- ttheme_minimal(
core=list(bg_params = list(fill = blues9[1:4], col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="orange", fontface=3L)))
# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
tableGrob(head(DiemTongKet, 10), theme = thm),
ncol=1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5321_diem.jpg", g)
# Tinh Trung vi mau, Cuc dai mau, Cuc tieu mau cua ky thi Giua ky
TrungViMauGK <- round(median(dataGK$TongDiemGK), 2)
CucDaiMauGK <- round(max(dataGK$TongDiemGK), 2)
CucTieuMauGK <- round(min(dataGK$TongDiemGK), 2)
# Tinh Trung vi mau, Cuc dai mau, Cuc tieu mau cua ky thi Giua ky
TrungViMauCK <- round(median(dataCK$TongDiemCK), 2)
CucDaiMauCK <- round(max(dataCK$TongDiemCK), 2)
CucTieuMauCK <- round(min(dataCK$TongDiemCK), 2)
# Tao 1 dataframe de plot table
df <- data.frame(
row.names = c("Giua ky", "Cuoi ky"),
TrungViMau = c(TrungViMauGK, TrungViMauCK),
CucDaiMau = c(CucDaiMauGK, CucDaiMauCK),
CucTieuMau = c(CucTieuMauGK, CucTieuMauCK)
)
# Theme cho table
thm <- ttheme_minimal(
core=list(bg_params = list(fill = blues9[1:4], col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="orange", fontface=3L)))
# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
tableGrob(df, theme = thm),
ncol=1
)

# Luu hinh anh vao thu muc hinh
ggsave("hinh/5311_medianmaxmin.jpg", g)
Saving 7.05 x 4.36 in image
# Dung subset de loai cac gia tri co N/A
SinhVienCoDiemGKLonHonBang9 <- nrow(subset(DiemTongKet, TongDiemGK >= 9))
SinhVienCoDiemCKLonHonBang9 <- nrow(subset(DiemTongKet, TongDiemCK >= 9))
sprintf("So sinh vien co diem Giua ky lon hon hoac bang 9 la %s", SinhVienCoDiemGKLonHonBang9)
[1] "So sinh vien co diem Giua ky lon hon hoac bang 9 la 0"
sprintf("So sinh vien co diem Cuoi ky lon hon hoac bang 9 la %s", SinhVienCoDiemCKLonHonBang9)
[1] "So sinh vien co diem Cuoi ky lon hon hoac bang 9 la 5"
SinhVienCoDiemGKLonHonBang7 <- nrow(subset(DiemTongKet, TongDiemGK >= 7))
SinhVienCoDiemCKLonHonBang7 <- nrow(subset(DiemTongKet, TongDiemCK >= 7))
sprintf("So sinh vien co diem Giua ky lon hon hoac bang 7 la %s", SinhVienCoDiemGKLonHonBang7)
[1] "So sinh vien co diem Giua ky lon hon hoac bang 7 la 77"
sprintf("So sinh vien co diem Cuoi ky lon hon hoac bang 7 la %s", SinhVienCoDiemCKLonHonBang7)
[1] "So sinh vien co diem Cuoi ky lon hon hoac bang 7 la 181"
SinhVienCoDiemGKLonHonBang5 <- nrow(subset(DiemTongKet, TongDiemGK >= 5))
SinhVienCoDiemCKLonHonBang5 <- nrow(subset(DiemTongKet, TongDiemCK >= 5))
sprintf("So sinh vien co diem Giua ky lon hon hoac bang 5 la %s", SinhVienCoDiemGKLonHonBang5)
[1] "So sinh vien co diem Giua ky lon hon hoac bang 5 la 311"
sprintf("So sinh vien co diem Cuoi ky lon hon hoac bang 5 la %s", SinhVienCoDiemCKLonHonBang5)
[1] "So sinh vien co diem Cuoi ky lon hon hoac bang 5 la 322"
SinhVienCoDiemGKNhoHon5 <-nrow(subset(DiemTongKet, TongDiemGK < 5))
SinhVienCoDiemCKNhoHon5 <- nrow(subset(DiemTongKet, TongDiemCK < 5))
sprintf("So sinh vien co diem Giua ky nho hon 5 la %s", SinhVienCoDiemGKNhoHon5)
[1] "So sinh vien co diem Giua ky nho hon 5 la 55"
sprintf("So sinh vien co diem Cuoi ky nho hon 5 la %s", SinhVienCoDiemCKNhoHon5)
[1] "So sinh vien co diem Cuoi ky nho hon 5 la 39"
# Plot diem Giua ky
# Tao 1 table moi dua tren col TongDiemGK de dem so occurrence cua moi diem Giua ky
dfDemDiemGK <- data.frame(table(dataGK$TongDiemGK))
colnames(dfDemDiemGK) <- c("DiemGK", "SoSV")
plotDiemGK <- ggplot(dfDemDiemGK, aes(x=DiemGK, y=SoSV)) +
geom_line(color="grey", aes(group=1)) +
geom_point(shape=21, color="black", fill="steelblue", size=6) + theme_ipsum() +
xlab("Diem") + ylab("So Sinh vien") +
theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
# Plot diem Cuoi ky
# Tao 1 table moi dua tren col TongDiemCK de dem so occurrence cua moi diem Cuoi ky
dfDemDiemCK <- data.frame(table(dataCK$TongDiemCK))
colnames(dfDemDiemCK) <- c("DiemCK", "SoSV")
plotDiemCK <- ggplot(dfDemDiemCK, aes(x=DiemCK, y=SoSV)) +
geom_line(color="grey", aes(group=1)) +
geom_point(shape=21, color="black", fill="coral1", size=6) + theme_ipsum() +
xlab("Diem") + ylab("So Sinh vien") +
theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
# Hien thi 2 bieu do
g <- grid.arrange(
arrangeGrob(plotDiemGK, bottom = "Diem Giua ky"),
arrangeGrob(plotDiemCK, bottom = "Diem Cuoi ky"),
nrow = 2, ncol = 1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5361_plotphodiemsinhvien.jpg", g)
# Tao 1 table moi tu dataGK voi tat ca cac column va voi cac row co TongDiemGK la cao nhat
SVDiemCaoNhatGK<- select(dataGK[dataGK[,"TongDiemGK"] == max(dataGK$TongDiemGK),], No, MANH, TO,TongDiemGK)
# Tao 1 table moi tu dataCK voi tat ca cac column va voi cac row co TongDiemCK la cao nhat
SVDiemCaoNhatCK<- select(dataCK[dataCK[,"TongDiemCK"] == max(dataCK$TongDiemCK),], No, MANH, TO,TongDiemCK)
# Theme cho table
thm <- ttheme_minimal(
core=list(bg_params = list(fill = blues9[1:4], col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="orange", fontface=3L)))
# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
tableGrob(head(SVDiemCaoNhatGK, 10,), theme = thm),
tableGrob(head(SVDiemCaoNhatCK, 10), theme = thm),
ncol=2, nrow=1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5371_diemcaonhat.jpg", g)
# Tao 1 table moi tu dataGK voi tat ca cac column va voi cac row co TongDiemGK la nho nhat
SVDiemNhoNhatGK<- select(dataGK[dataGK[,"TongDiemGK"] == min(dataGK$TongDiemGK),], No, MANH, TO,TongDiemGK)
# Tao 1 table moi tu dataGK voi tat ca cac column va voi cac row co TongDiemGK la nho nhat
SVDiemNhoNhatCK<- select(dataCK[dataCK[,"TongDiemCK"] == min(dataCK$TongDiemCK),], No, MANH, TO,TongDiemCK)
# Theme cho table
thm <- ttheme_minimal(
core=list(bg_params = list(fill = blues9[1:4], col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="orange", fontface=3L)))
# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
tableGrob(head(SVDiemNhoNhatGK, 10,), theme = thm),
tableGrob(head(SVDiemNhoNhatCK, 10), theme = thm),
ncol=2, nrow=1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5381_diemthapnhat.jpg", g)
DiemTrungBinhGK <- round(mean(DiemCuaMoiSVGK$TongDiemGK),1)
DiemTrungBinhCK <- round(mean(DiemCuaMoiSVCK$TongDiemCK),1)
sprintf("Diem trung binh cua cac sinh vien trong tap mau Giua ky la %s", DiemTrungBinhGK)
[1] "Diem trung binh cua cac sinh vien trong tap mau Giua ky la 6.1"
sprintf("Diem trung binh cua cac sinh vien trong tap mau Cuoi ky la %s", DiemTrungBinhCK)
[1] "Diem trung binh cua cac sinh vien trong tap mau Cuoi ky la 6.8"
SVCoDiemTrungBinhGK <-nrow(select(dataGK[dataGK[,"TongDiemGK"] == DiemTrungBinhGK,], No, MANH, TO,TongDiemGK))
SVCoDiemTrungBinhCK <-nrow(select(dataCK[dataCK[,"TongDiemCK"] == DiemTrungBinhCK,], No, MANH, TO,TongDiemCK))
sprintf("So sinh vien co diem trung binh %s trong tap mau Giua ky la %s", DiemTrungBinhGK, SVCoDiemTrungBinhGK)
[1] "So sinh vien co diem trung binh 6.1 trong tap mau Giua ky la 0"
sprintf("So sinh vien co diem trung binh %s trong tap mau Cuoi ky la %s", DiemTrungBinhCK, SVCoDiemTrungBinhCK)
[1] "So sinh vien co diem trung binh 6.8 trong tap mau Cuoi ky la 0"
# Xac dinh do phan tan cua diem so chinh la Standard Deviation
DoPhanTanGK <- sd(DiemCuaMoiSVGK$TongDiemGK)
DoPhanTanCK <- sd(DiemCuaMoiSVCK$TongDiemCK)
# Tao dataframe de chuan bi plot bieu do
dfPhanTan <- data.frame(
KyThi = c("Giua ky", "Cuoi ky"),
DiemTrungBinh = c(DiemTrungBinhGK, DiemTrungBinhCK),
DoPhanTan = c(DoPhanTanGK, DoPhanTanCK)
)
g <- ggplot(dfPhanTan, aes(x=KyThi, y=DiemTrungBinh)) +
geom_errorbar(aes(ymin=round(DiemTrungBinh - DoPhanTan, 2), ymax=round(DiemTrungBinh + DoPhanTan, 2)), width=0.2, color="coral1") +
geom_text(aes(label = round(DiemTrungBinh - DoPhanTan, 2), y = round(DiemTrungBinh - DoPhanTan, 2)), vjust = 1.5) +
geom_text(aes(label = round(DiemTrungBinh + DoPhanTan, 2), y = round(DiemTrungBinh + DoPhanTan, 2)), vjust = -.5) +
geom_point(shape=21, color="black", fill="steelblue", size=6) +
geom_text(aes(label=round(DoPhanTan, 2)),hjust=1.5, vjust=0, color="coral1") +
labs(y="Muc do phan tan cua diem so", x="Ky thi")
# Luu hinh anh vao thu muc hinh
# ggsave("hinh/53111_mucdophantan.jpg", g)
# Cau III.12
# Tinh do meo lech cua du lieu bang ham skewness
skewnessGK <- skewness(DiemCuaMoiSVGK$TongDiemGK)
skewnessCK <- skewness(DiemCuaMoiSVCK$TongDiemCK)
sprintf("Do meo lech (skewness) cua du lieu trong mau Giua ky la %s", skewnessGK)
[1] "Do meo lech (skewness) cua du lieu trong mau Giua ky la -0.413641637265747"
sprintf("Do meo lech (skewness) cua du lieu trong mau Cuoi ky la %s", skewnessCK)
[1] "Do meo lech (skewness) cua du lieu trong mau Cuoi ky la -0.813640677743373"
# Tinh do nhon cua du lieu bang ham kurtosis
kurtosisGK <- kurtosis(DiemCuaMoiSVGK$TongDiemGK)
kurtosisCK <- kurtosis(DiemCuaMoiSVCK$TongDiemCK)
sprintf("Do nhon (kurtosis) cua du lieu trong mau Giua ky la %s", kurtosisGK)
[1] "Do nhon (kurtosis) cua du lieu trong mau Giua ky la 3.19521442229673"
sprintf("Do nhon (kurtosis) cua du lieu trong mau Cuoi ky la %s", kurtosisCK)
[1] "Do nhon (kurtosis) cua du lieu trong mau Cuoi ky la 3.17404347172822"
# Cau III.13
# Giat thich Q1 va Q3
# 0% -> 25% -> 50% -> 75% -> 100%
# min -> Q1 -> mean -> Q3 -> max
# Tinh Q1 & Q3 cho tap mau Giua ky
Q1GK = quantile(DiemCuaMoiSVGK$TongDiemGK,0.25)
Q3GK = quantile(DiemCuaMoiSVGK$TongDiemGK,0.75)
sprintf("Tu phan vi (quartile) thu nhat (Q1) va thu ba (Q3) cua tap mau Giua ky lan luot la %s va %s", Q1GK, Q3GK)
[1] "Tu phan vi (quartile) thu nhat (Q1) va thu ba (Q3) cua tap mau Giua ky lan luot la 5.2 va 6.8"
# Tinh Q1 & Q3 cho tap mau Cuoi ky
Q1CK = quantile(DiemCuaMoiSVCK$TongDiemCK,0.25)
Q3CK = quantile(DiemCuaMoiSVCK$TongDiemCK,0.75)
sprintf("Tu phan vi (quartile) thu nhat (Q1) va thu ba (Q3) cua tap mau Cuoi ky lan luot la %s va %s", Q1CK, Q3CK)
[1] "Tu phan vi (quartile) thu nhat (Q1) va thu ba (Q3) cua tap mau Cuoi ky lan luot la 5.9 va 7.9"
# Cau III.14
# Tao ham tinh ra table chua ca sinh vien co muc diem Giua ky cao thu K
GKK <- function(k){
temp<-table(round(TongSoCauDungCuaSVGK$SoCauDung/25*10,1))
options(digits = 2)
muc <- as.double(names(temp[length(temp)- k + 1]))
DS <- dataGK[dataGK[,"TongDiemGK"] == muc,]
return(DS)
}
# Tao ham tinh ra table chua ca sinh vien co muc diem Cuoi ky cao thu K
CKK <- function(k){
temp<-table(round(TongSoCauDungCuaSVCK$SoCauDung/29*10,1))
options(digits = 2)
muc <- as.double(names(temp[length(temp)- k + 1]))
DS <- dataCK[dataCK[,"TongDiemCK"] == muc,]
return(DS)
}
# Tao table voi tat ca column va lay nhung row co TongDiemGK o muc diem cao nhat trong tap mau Giua ky va Cuoi ky
DSSVQ3GK <- rbind(GKK(1), GKK(2))
DSSVQ3CK <- rbind(CKK(1), CKK(2))
# So luong sinh vien chinh la so dong (row) cua table moi tao
SoSVQ3GK = nrow(DSSVQ3GK)
SoSVQ3CK = nrow(DSSVQ3CK)
sprintf("So sinh vien co diem so nam trong 2 muc diem cao nhat trong tap Giua ky la %s", SoSVQ3GK)
[1] "So sinh vien co diem so nam trong 2 muc diem cao nhat trong tap Giua ky la 14"
sprintf("So sinh vien co diem so nam trong 2 muc diem cao nhat trong tap Cuoi ky la %s", SoSVQ3CK)
[1] "So sinh vien co diem so nam trong 2 muc diem cao nhat trong tap Cuoi ky la 24"
# Cau III.15
# Plot diem Giua ky
# Tao 1 table moi dua tren col TongDiemGK de dem so occurrence cua moi diem lon hon Q3 cua tap mau Giua ky
dfDemDiemQ3GK <- data.frame(table(DSSVQ3GK$TongDiemGK))
colnames(dfDemDiemQ3GK) <- c("DiemGK", "SoSV")
plotDiemQ3GK <- ggplot(dfDemDiemQ3GK, aes(x=DiemGK, y=SoSV)) +
geom_line(color="grey", aes(group=1)) +
geom_point(shape=21, color="black", fill="steelblue", size=6) + theme_ipsum() +
xlab("Diem") + ylab("So Sinh vien") +
theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
# Plot diem cuoi ky
# Tao 1 table moi dua tren col TongDiemCK de dem so occurrence cua moi diem lon hon Q3 cua tap mau Cuoi ky
dfDemDiemQ3CK <- data.frame(table(DSSVQ3CK$TongDiemCK))
colnames(dfDemDiemQ3CK) <- c("DiemCK", "SoSV")
plotDiemQ3CK <- ggplot(dfDemDiemQ3CK, aes(x=DiemCK, y=SoSV)) +
geom_line(color="grey", aes(group=1)) +
geom_point(shape=21, color="black", fill="coral1", size=6) + theme_ipsum() +
xlab("Diem") + ylab("So Sinh vien") +
theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
# Hien thi 2 bieu do
g <- grid.arrange(
arrangeGrob(plotDiemQ3GK, bottom = "Diem Giua ky"),
arrangeGrob(plotDiemQ3CK, bottom = "Diem Cuoi ky"),
nrow = 2, ncol = 1
)

g
TableGrob (2 x 1) "arrange": 2 grobs
# Luu hinh anh vao thu muc hinh
ggsave("hinh/53151_plotphodiemsinhvienQ3.jpg", g)
Saving 10 x 10 in image
# Cau III.16
# Tan dung lai 2 ham GKK & CKK de tinh so sinh vien co muc diem cao thu K
# Dat k = 5
k = 5
# Tao table voi tat ca column va lay nhung row co TongDiemGK o muc diem cao thu k trong tap mau Giua ky va Cuoi ky
DSSV_K_GK <- GKK(k)
DSSV_K_CK <- CKK(k)
# So luong sinh vien chinh la so dong (row) cua table moi tao
SoSV_K_GK = nrow(DSSV_K_GK)
SoSV_K_CK = nrow(DSSV_K_CK)
sprintf("So sinh vien co diem so voi muc diem cao thu %s trong tap Giua ky la %s", k, SoSV_K_GK)
[1] "So sinh vien co diem so voi muc diem cao thu 5 trong tap Giua ky la 37"
sprintf("So sinh vien co diem so voi muc diem cao thu %s trong tap Cuoi ky la %s", k, SoSVQ3CK)
[1] "So sinh vien co diem so voi muc diem cao thu 5 trong tap Cuoi ky la 24"
# Cau III.17
# Plot diem Giua ky
# Tao 1 table moi dua tren col TongDiemGK de dem so occurrence cua moi diem lon hon Q3 cua tap mau Giua ky
plotDemDiemK <- function(kythi, dssv) {
if (kythi == "GK") {
dfDemDiemK <- data.frame(table(dssv$TongDiemGK))
colnames(dfDemDiemK) <- c("DiemGK", "SoSV")
return(
ggplot(dfDemDiemK, aes(x=DiemGK, y=SoSV)) +
geom_line(color="grey", aes(group=1)) +
geom_point(shape=21, color="black", fill="steelblue", size=6) + theme_ipsum() +
xlab("Diem") + ylab("So Sinh vien") +
theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
)
} else if (kythi == "CK") {
dfDemDiemK <- data.frame(table(dssv$TongDiemCK))
colnames(dfDemDiemK) <- c("DiemCK", "SoSV")
return(
ggplot(dfDemDiemK, aes(x=DiemCK, y=SoSV)) +
geom_line(color="grey", aes(group=1)) +
geom_point(shape=21, color="black", fill="coral1", size=6) + theme_ipsum() +
xlab("Diem") + ylab("So Sinh vien") +
theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
)
}
}
# Chon 3 K lan luoi bang 5, 3, 9 cho ca 2 tap mau Giua ky va Cuoi ky
dssv_k5_GK <- GKK(5)
dssv_k3_GK <- GKK(3)
dssv_k9_GK <- GKK(9)
dssv_GK <- rbind(dssv_k3_GK, dssv_k5_GK, dssv_k9_GK)
plotDiem_K_GK <- plotDemDiemK("GK", dssv_GK)
dssv_k5_CK <- CKK(5)
dssv_k3_CK <- CKK(3)
dssv_k9_CK <- CKK(9)
dssv_CK <- rbind(dssv_k3_CK, dssv_k5_CK, dssv_k9_CK)
plotDiem_K_CK <- plotDemDiemK("CK", dssv_CK)
# Ve table chua K
# Theme cho table
thm <- ttheme_minimal(
core=list(bg_params = list(fill = blues9[1:4], col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="orange", fontface=3L)))
dfTableK <- data.frame(
k = c("3", "5", "9"),
DiemGK = c(dssv_k3_GK$TongDiemGK[[1]], dssv_k5_GK$TongDiemGK[[1]], dssv_k9_GK$TongDiemGK[[1]]),
DiemCK = c(dssv_k3_CK$TongDiemCK[[1]], dssv_k5_CK$TongDiemCK[[1]], dssv_k9_CK$TongDiemCK[[1]])
)
# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
tableGrob(dfTableK, theme = thm),
ncol=1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/53171_thongtink.jpg", g)
# Hien thi 2 bieu do
grid.newpage()

g <- grid.arrange(
arrangeGrob(plotDiem_K_GK, bottom = "Diem Giua ky"),
arrangeGrob(plotDiem_K_CK, bottom = "Diem Cuoi ky"),
nrow = 2, ncol = 1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/53172_plotphodiemsinhvienK.jpg", g)
Phan IV
# Import du lieu thi Giua ky
DFGK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 3, startRow = 1, endRow = 16)
# Import du lieu thi Cuoi ky
DFCK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 5, startRow = 1, endRow = 16)
# Xac dinh cac chuan dau ra xuat hien trong tap mau Giua ky
GKCDR <- DFGK[7:10, 1:26]
# Tao ra 1 matrix co chieu dai 1 x n (voi n la column tu X1 - X25)
GKCDR.1921 <- GKCDR %>% filter(GKCDR[1] == "1921") %>% select(X1:X25) %>% apply(2,as.numeric)
GKCDR.1922 <- GKCDR %>% filter(GKCDR[1] == "1922") %>% select(X1:X25) %>% apply(2,as.numeric)
GKCDR.1923 <- GKCDR %>% filter(GKCDR[1] == "1923") %>% select(X1:X25) %>% apply(2,as.numeric)
GKCDR.1924 <- GKCDR %>% filter(GKCDR[1] == "1924") %>% select(X1:X25) %>% apply(2,as.numeric)
# Xac dinh cac chuan dau ra xuat hien trong tap mau Cuoi ky
CKCDR <- DFCK[7:10, 1:30]
# Tao ra 1 matrix co chieu dai 1 x n (voi n la column tu X1 - X29)
CKCDR.1921 <- CKCDR %>% filter(CKCDR[1] == "1921") %>% select(X1:X29) %>% apply(2,as.numeric)
CKCDR.1922 <- CKCDR %>% filter(CKCDR[1] == "1922") %>% select(X1:X29) %>% apply(2,as.numeric)
CKCDR.1923 <- CKCDR %>% filter(CKCDR[1] == "1923") %>% select(X1:X29) %>% apply(2,as.numeric)
CKCDR.1924 <- CKCDR %>% filter(CKCDR[1] == "1924") %>% select(X1:X29) %>% apply(2,as.numeric)
# Cau IV.1
# table(GKCDR.1921) se group cac chuan dau ra lai cung voi count cua tung chuan dau ra.
# So luong chuan dau ra lien quan den cac cau hoi xuat hien trong tap mau Giua ky
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1921 trong tap mau Giua ky la %s", length(names(table(GKCDR.1921))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1921 trong tap mau Giua ky la 5"
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1922 trong tap mau Giua ky la %s", length(names(table(GKCDR.1922))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1922 trong tap mau Giua ky la 6"
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1923 trong tap mau Giua ky la %s", length(names(table(GKCDR.1923))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1923 trong tap mau Giua ky la 5"
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1924 trong tap mau Giua ky la %s", length(names(table(GKCDR.1924))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1924 trong tap mau Giua ky la 5"
# So luong chuan dau ra lien quan den cac cau hoi xuat hien trong tap mau Cuoi ky
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1921 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1921))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1921 trong tap mau Cuoi ky la 4"
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1922 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1922))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1922 trong tap mau Cuoi ky la 4"
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1923 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1923))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1923 trong tap mau Cuoi ky la 4"
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1924 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1924))))
[1] "So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1924 trong tap mau Cuoi ky la 4"
# Cau IV.2
# Danh sach cac chuan dau ra chi tiet lien quan den cac cau hoi xuat hien trong tap mau Giua ky
# NOTE: names([table-object]) -> lay tat ca
# NOTE: paste([list-object], collapse = ", ") => chuyen list thanh string cach nhau bang dau ", "
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1921 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1921)), collapse = ', '))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1921 trong tap mau Giua ky lan luot la 11, 12, 21, 22, 31"
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1922 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1922)), collapse = ', '))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1922 trong tap mau Giua ky lan luot la 11, 12, 21, 22, 23, 31"
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1923 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1923)), collapse = ', '))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1923 trong tap mau Giua ky lan luot la 11, 12, 21, 22, 31"
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1924 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1924)), collapse = ','))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1924 trong tap mau Giua ky lan luot la 11,12,21,22,31"
# Danh sach cac chuan dau ra chi tiet lien quan den cac cau hoi xuat hien trong tap mau Cuoi ky
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1921 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1921)), collapse = ', '))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1921 trong tap mau Cuoi ky lan luot la 12, 23, 31, 32"
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1922 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1922)), collapse = ', '))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1922 trong tap mau Cuoi ky lan luot la 12, 23, 31, 32"
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1923 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1923)), collapse = ', '))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1923 trong tap mau Cuoi ky lan luot la 12, 23, 31, 32"
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1924 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1924)), collapse = ', '))
[1] "Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1924 trong tap mau Cuoi ky lan luot la 12, 23, 31, 32"
# Cau IV.3
#Graph Tan suat cua Chuan dau ra o tap mau Giua ky
G3GK.data1921 <- as.data.frame(table(GKCDR.1921))
G3GK.data1922 <- as.data.frame(table(GKCDR.1922))
G3GK.data1923 <- as.data.frame(table(GKCDR.1923))
G3GK.data1924 <- as.data.frame(table(GKCDR.1924))
#MD 1921, 1923, 1924 share mutual frequency
G3GK1921 <- ggplot(G3GK.data1921, aes(x = GKCDR.1921,y =G3GK.data1921[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1921[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1921 ", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "#ffaa80"))
G3GK1922 <- ggplot(G3GK.data1922, aes(x = GKCDR.1922,y =G3GK.data1922[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1922[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "#ffaa80"))
G3GK1923 <- ggplot(G3GK.data1923, aes(x = GKCDR.1923,y =G3GK.data1923[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1923[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "#ffaa80"))
G3GK1924 <- ggplot(G3GK.data1924, aes(x = GKCDR.1924,y =G3GK.data1924[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1924[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "#ffaa80"))
G3GK <- ggarrange(G3GK1921, G3GK1922, G3GK1923, G3GK1924, ncol = 2, nrow = 2)
G3GK

# ggsave("hinh/5431_plotchuandauragk.jpg", G3GK)
#Graph Tan suat cua Chuan dau ra o tap mau Cuoi ky
G3CK.data1921 <- as.data.frame(table(CKCDR.1921))
G3CK.data1922 <- as.data.frame(table(CKCDR.1922))
G3CK.data1923 <- as.data.frame(table(CKCDR.1923))
G3CK.data1924 <- as.data.frame(table(CKCDR.1924))
#MD 1921, 1923, 1924 share mutual frequency
G3CK1921 <- ggplot(G3CK.data1921, aes(x = CKCDR.1921,y =G3CK.data1921[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1921[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1921 ", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "cornsilk"))
G3CK1922 <- ggplot(G3CK.data1922, aes(x = CKCDR.1922,y =G3CK.data1922[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1922[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "cornsilk"))
G3CK1923 <- ggplot(G3CK.data1923, aes(x = CKCDR.1923,y =G3CK.data1923[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1923[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "cornsilk"))
G3CK1924 <- ggplot(G3CK.data1924, aes(x = CKCDR.1924,y =G3CK.data1924[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1924[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
theme(panel.background = element_rect(fill = "cornsilk"))
G3CK <- ggarrange(G3CK1921, G3CK1922, G3CK1923, G3CK1924, ncol = 2, nrow = 2)
G3CK

# ggsave("hinh/5432_plotchuandaurack.jpg", G3GK)
# Tao dataframe tu file Excel
DiemGK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 2, startRow = 5)
# Don cac truong co ky tu rong
DiemGK <- DiemGK %>% filter(No != "")
# Tao matrix moi
DiemGK <- subset(DiemGK, select = No:MADE)
DiemGK.1921 <- DiemGK %>% filter(MADE == 1921) %>% select(X1:X25) %>% apply(2,as.numeric)
DiemGK.1922 <- DiemGK %>% filter(MADE == 1922) %>% select(X1:X25) %>% apply(2,as.numeric)
DiemGK.1923 <- DiemGK %>% filter(MADE == 1923) %>% select(X1:X25) %>% apply(2,as.numeric)
DiemGK.1924 <- DiemGK %>% filter(MADE == 1924) %>% select(X1:X25) %>% apply(2,as.numeric)
#Add Learning outcome at top of data frame
DiemGK.1921.LO <- as.data.frame(rbind(GKCDR.1921,DiemGK.1921))
DiemGK.1922.LO <- as.data.frame(rbind(GKCDR.1922,DiemGK.1922))
DiemGK.1923.LO <- as.data.frame(rbind(GKCDR.1923,DiemGK.1923))
DiemGK.1924.LO <- as.data.frame(rbind(GKCDR.1924,DiemGK.1924))
#Learning outcome name in midterm
names(table(GKCDR.1921))
[1] "11" "12" "21" "22" "31"
#1921
# Phan loai theo cac chuan dau ra
DiemGK.1921.LO11 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 11]
DiemGK.1921.LO12 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 12]
DiemGK.1921.LO21 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 21]
DiemGK.1921.LO22 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 22]
DiemGK.1921.LO31 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 31]
# Cong tong theo dong cua dataframe va them Tong moi vao trong data
DiemGK.1921.LO11 <-table(apply(DiemGK.1921.LO11, 1, sum))
DiemGK.1921.LO12 <-table(apply(DiemGK.1921.LO12, 1, sum))
DiemGK.1921.LO21 <-table(apply(DiemGK.1921.LO21, 1, sum))
DiemGK.1921.LO22 <-table(apply(DiemGK.1921.LO22, 1, sum))
DiemGK.1921.LO31 <-table(apply(DiemGK.1921.LO31, 1, sum))
#1922
#Sorting answer into learning outcome group
DiemGK.1922.LO11 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 11]
DiemGK.1922.LO12 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 12]
DiemGK.1922.LO21 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 21]
DiemGK.1922.LO22 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 22]
DiemGK.1922.LO31 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 31]
DiemGK.1922.LO23 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 23]
#Sum row data frame and map sum result into instance group
DiemGK.1922.LO11 <-table(apply(DiemGK.1922.LO11, 1, sum))
DiemGK.1922.LO12 <-table(apply(DiemGK.1922.LO12, 1, sum))
DiemGK.1922.LO21 <-table(apply(DiemGK.1922.LO21, 1, sum))
DiemGK.1922.LO22 <-table(apply(DiemGK.1922.LO22, 1, sum))
DiemGK.1922.LO31 <-table(apply(DiemGK.1922.LO31, 1, sum))
DiemGK.1922.LO23 <-table(DiemGK.1922.LO23)
#1923
#Sorting answer into learning outcome group
DiemGK.1923.LO11 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 11]
DiemGK.1923.LO12 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 12]
DiemGK.1923.LO21 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 21]
DiemGK.1923.LO22 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 22]
DiemGK.1923.LO31 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 31]
DiemGK.1923.LO31 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 31]
#Sum row data frame and map sum result into instance group
DiemGK.1923.LO11 <-table(apply(DiemGK.1923.LO11, 1, sum))
DiemGK.1923.LO12 <-table(apply(DiemGK.1923.LO12, 1, sum))
DiemGK.1923.LO21 <-table(apply(DiemGK.1923.LO21, 1, sum))
DiemGK.1923.LO22 <-table(apply(DiemGK.1923.LO22, 1, sum))
DiemGK.1923.LO31 <-table(apply(DiemGK.1923.LO31, 1, sum))
#1924
# Phan loai theo cac chuan dau ra
DiemGK.1924.LO11 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 11]
DiemGK.1924.LO12 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 12]
DiemGK.1924.LO21 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 21]
DiemGK.1924.LO22 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 22]
DiemGK.1924.LO31 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 31]
# Cong tong theo dong cua dataframe va them Tong moi vao trong data
DiemGK.1924.LO11 <-table(apply(DiemGK.1924.LO11, 1, sum))
DiemGK.1924.LO12 <-table(apply(DiemGK.1924.LO12, 1, sum))
DiemGK.1924.LO21 <-table(apply(DiemGK.1924.LO21, 1, sum))
DiemGK.1924.LO22 <-table(apply(DiemGK.1924.LO22, 1, sum))
DiemGK.1924.LO31 <-table(apply(DiemGK.1924.LO31, 1, sum))
# Learning outcome 11
LOR.GK11 <- as.data.frame(bind_rows(DiemGK.1921.LO11,DiemGK.1922.LO11,DiemGK.1923.LO11,DiemGK.1924.LO11))
# Thay cac gia tri N/A thanh 0
LOR.GK11[is.na(LOR.GK11)] <- 0
# Cong theo dong
LOR.GK11 <- as.data.frame(apply(LOR.GK11,2,sum))
LOR.GK11$SoCauTLDung <- row.names(LOR.GK11)
# xap sep lai data
LOR.GK11<- transform(LOR.GK11, SoCauTLDung = as.numeric(SoCauTLDung))
LOR.GK11<- arrange(LOR.GK11,SoCauTLDung)
#Learning outcome 12 (merge into usable data frame)
LOR.GK12 <- as.data.frame(bind_rows(DiemGK.1921.LO12,DiemGK.1922.LO12,DiemGK.1923.LO12,DiemGK.1924.LO12))
#Fill N/A cell with 0
LOR.GK12[is.na(LOR.GK12)] <- 0
#Sum column
LOR.GK12 <- as.data.frame(apply(LOR.GK12,2,sum))
LOR.GK12$SoCauTLDung <- row.names(LOR.GK12)
#Learning outcome 21 (merge into usable data frame)
LOR.GK21 <- as.data.frame(bind_rows(DiemGK.1921.LO21,DiemGK.1922.LO21,DiemGK.1923.LO21,DiemGK.1924.LO21))
#Fill N/A cell with 0
LOR.GK21[is.na(LOR.GK21)] <- 0
#Sum column
LOR.GK21 <- as.data.frame(apply(LOR.GK21,2,sum))
LOR.GK21$SoCauTLDung <- row.names(LOR.GK21)
#Learning outcome 22 (merge into usable data frame)
LOR.GK22 <- as.data.frame(bind_rows(DiemGK.1921.LO22,DiemGK.1922.LO22,DiemGK.1923.LO22,DiemGK.1924.LO22))
#Fill N/A cell with 0
LOR.GK22[is.na(LOR.GK22)] <- 0
#Sum column
LOR.GK22 <- as.data.frame(apply(LOR.GK22,2,sum))
LOR.GK22$SoCauTLDung <- row.names(LOR.GK22)
#Learning outcome 31 (merge into usable data frame)
LOR.GK31 <- as.data.frame(bind_rows(DiemGK.1921.LO31,DiemGK.1922.LO31,DiemGK.1923.LO31,DiemGK.1924.LO31))
#Fill N/A cell with 0
LOR.GK31[is.na(LOR.GK31)] <- 0
#Sum column
LOR.GK31 <- as.data.frame(apply(LOR.GK31,2,sum))
LOR.GK31$SoCauTLDung <- row.names(LOR.GK31)
#Learning outcome 23
LOR.GK23 <-as.data.frame(DiemGK.1922.LO23)
LOR.GK23$SoCauTraLoiDung <- LOR.GK23[,1]
GLOR.GK11 <- ggplot(LOR.GK11, aes(x = as.character(LOR.GK11[,2]), y = LOR.GK11[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK11[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 11", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOR.GK12 <- ggplot(LOR.GK12, aes(x = as.character(LOR.GK12[,2]), y = LOR.GK12[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK12[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 12", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOR.GK21 <- ggplot(LOR.GK21, aes(x = as.character(LOR.GK21[,2]), y = LOR.GK21[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK21[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 21", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOR.GK22 <- ggplot(LOR.GK22, aes(x = as.character(LOR.GK22[,2]), y = LOR.GK22[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK22[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 22", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOR.GK31 <- ggplot(LOR.GK31, aes(x = as.character(LOR.GK31[,2]), y = LOR.GK31[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK31[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 31", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOR.GK23 <- ggplot(LOR.GK23, aes(x = as.character(LOR.GK23[,3]), y = LOR.GK23[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK23[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 23", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOR.GK <-ggarrange(GLOR.GK11,GLOR.GK12,GLOR.GK21,GLOR.GK22,GLOR.GK23,GLOR.GK31, ncol = 3, nrow = 2)
GLOR.GK

# ggsave("hinh/5451_bieudooutcome_gk.jpg", GLOR.GK)
# Tao dataframe tu file excel cho tap mau Cuoi ky
DiemCK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 4, startRow = 5)
# Loc No rong
DiemCK <- DiemCK %>% filter(No != "")
# Tao matrix
DiemCK <- subset(DiemCK, select = No:MADE)
DiemCK.1921 <- DiemCK %>% filter(MADE == 1921) %>% select(X1:X29) %>% apply(2,as.numeric)
DiemCK.1922 <- DiemCK %>% filter(MADE == 1922) %>% select(X1:X29) %>% apply(2,as.numeric)
DiemCK.1923 <- DiemCK %>% filter(MADE == 1923) %>% select(X1:X29) %>% apply(2,as.numeric)
DiemCK.1924 <- DiemCK %>% filter(MADE == 1924) %>% select(X1:X29) %>% apply(2,as.numeric)
#Add Learning outcome at top of data frame
DiemCK.1921.LO <- as.data.frame(rbind(CKCDR.1921,DiemCK.1921))
DiemCK.1922.LO <- as.data.frame(rbind(CKCDR.1922,DiemCK.1922))
DiemCK.1923.LO <- as.data.frame(rbind(CKCDR.1923,DiemCK.1923))
DiemCK.1924.LO <- as.data.frame(rbind(CKCDR.1924,DiemCK.1924))
#Learning outcome name in final exam
names(table(CKCDR.1921))
[1] "12" "23" "31" "32"
#1921
#S Phan loai theo cac chuan dau ra
DiemCK.1921.LO12 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 12]
DiemCK.1921.LO23 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 23]
DiemCK.1921.LO31 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 31]
DiemCK.1921.LO32 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 32]
# Cong tong theo dong cua dataframe va them Tong moi vao trong data
DiemCK.1921.LO12 <-table(apply(DiemCK.1921.LO12, 1, sum))
DiemCK.1921.LO23 <-table(apply(DiemCK.1921.LO23, 1, sum))
DiemCK.1921.LO31 <-table(apply(DiemCK.1921.LO31, 1, sum))
DiemCK.1921.LO32 <-table(apply(DiemCK.1921.LO32, 1, sum))
#1922
#Sorting answer into learning outcome group
DiemCK.1922.LO12 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 12]
DiemCK.1922.LO23 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 23]
DiemCK.1922.LO31 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 31]
DiemCK.1922.LO32 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 32]
#Sum row data frame and map sum result into instance group
DiemCK.1922.LO12 <-table(apply(DiemCK.1922.LO12, 1, sum))
DiemCK.1922.LO23 <-table(apply(DiemCK.1922.LO23, 1, sum))
DiemCK.1922.LO31 <-table(apply(DiemCK.1922.LO31, 1, sum))
DiemCK.1922.LO32 <-table(apply(DiemCK.1922.LO32, 1, sum))
#1923
#Sorting answer into learning outcome group
DiemCK.1923.LO12 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 12]
DiemCK.1923.LO23 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 23]
DiemCK.1923.LO31 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 31]
DiemCK.1923.LO32 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 32]
#Sum row data frame and map sum result into instance group
DiemCK.1923.LO12 <-table(apply(DiemCK.1923.LO12, 1, sum))
DiemCK.1923.LO23 <-table(apply(DiemCK.1923.LO23, 1, sum))
DiemCK.1923.LO31 <-table(apply(DiemCK.1923.LO31, 1, sum))
DiemCK.1923.LO32 <-table(apply(DiemCK.1923.LO32, 1, sum))
#1924
#Sorting answer into learning outcome group
DiemCK.1924.LO12 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 12]
DiemCK.1924.LO23 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 23]
DiemCK.1924.LO31 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 31]
DiemCK.1924.LO32 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 32]
#Sum row data frame and map sum result into instance group
DiemCK.1924.LO12 <-table(apply(DiemCK.1924.LO12, 1, sum))
DiemCK.1924.LO23 <-table(apply(DiemCK.1924.LO23, 1, sum))
DiemCK.1924.LO31 <-table(apply(DiemCK.1924.LO31, 1, sum))
DiemCK.1924.LO32 <-table(apply(DiemCK.1924.LO32, 1, sum))
#Learning outcome 12
LOR.CK12 <- as.data.frame(bind_rows(DiemCK.1921.LO12,DiemCK.1922.LO12,DiemCK.1923.LO12,DiemCK.1924.LO12))
# Thay cac gia tri N/A thanh 0
LOR.CK12[is.na(LOR.CK12)] <- 0
# Cong cot
LOR.CK12 <- as.data.frame(apply(LOR.CK12,2,sum))
# Sap xep lai data
LOR.CK12$SoCauTLDung <- row.names(LOR.CK12)
#Learning outcome 23 (merge into usable data frame)
LOR.CK23 <- as.data.frame(bind_rows(DiemCK.1921.LO23,DiemCK.1922.LO23,DiemCK.1923.LO23,DiemCK.1924.LO23))
#Fill N/A cell with 0
LOR.CK23[is.na(LOR.CK23)] <- 0
#Sum column
LOR.CK23 <- as.data.frame(apply(LOR.CK23,2,sum))
LOR.CK23$SoCauTLDung <- row.names(LOR.CK23)
#Learning outcome 31 (merge into usable data frame)
LOR.CK31 <- as.data.frame(bind_rows(DiemCK.1921.LO31,DiemCK.1922.LO31,DiemCK.1923.LO31,DiemCK.1924.LO31))
#Fill N/A cell with 0
LOR.CK31[is.na(LOR.CK31)] <- 0
#Sum column
LOR.CK31 <- as.data.frame(apply(LOR.CK31,2,sum))
LOR.CK31$SoCauTLDung <- row.names(LOR.CK31)
LOR.CK31
#Learning outcome 32 (merge into usable data frame)
LOR.CK32 <- as.data.frame(bind_rows(DiemCK.1921.LO32,DiemCK.1922.LO32,DiemCK.1923.LO32,DiemCK.1924.LO32))
#Fill N/A cell with 0
LOR.CK32[is.na(LOR.CK32)] <- 0
#Sum column
LOR.CK32 <- as.data.frame(apply(LOR.CK32,2,sum))
LOR.CK32$SoCauTLDung <- row.names(LOR.CK32)
#Graph ----
GLOR.CK12 <-ggplot(LOR.CK12, aes(x = as.character(LOR.CK12[,2]), y = LOR.CK12[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK12[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 12", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))
GLOR.CK23 <- ggplot(LOR.CK23, aes(x = as.character(LOR.CK23[,2]), y = LOR.CK23[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK23[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 23", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))
GLOR.CK31 <-ggplot(LOR.CK31, aes(x = as.character(LOR.CK31[,2]), y = LOR.CK31[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK31[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 31", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))
GLOR.CK32 <- ggplot(LOR.CK32, aes(x = as.character(LOR.CK32[,2]), y = LOR.CK32[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK32[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 32", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))
GLOR.CK <-ggarrange(GLOR.CK12,GLOR.CK23,GLOR.CK31,GLOR.CK32, ncol = 2, nrow = 2)
GLOR.CK

ggsave("hinh/5452_bieudooutcome_ck.jpg", GLOR.CK)
Saving 15 x 15 in image
# Cau IV.7
# Data cho Chuan dau ra Giua ky va Cuoi ky
LO.data<- as.data.frame(bind_rows(table(GKCDR.1921),table(CKCDR.1921)))
# Chuuyen doi thanh data dung de ve bieu do
LO.data[is.na(LO.data)] <- 0
LO.data<- as.data.frame(apply(LO.data,2,sum))
LO.data$LO <- row.names(LO.data)
LO.data
# Do thi
GLO.data <- ggplot(LO.data, aes(x = as.character(LO.data[,2]), y = LO.data[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LO.data[,1]), vjust=-0.3, size=3.5, )+
labs(title = "Question per learning outcome", x = "Learning outcome", y = "Questions quantity") + theme(panel.background = element_rect(fill = "#57d5c5"))
GLO.data
ggsave("hinh/5471_bieudocauhoi.jpg", GLO.data)
Saving 7.05 x 4.36 in image

#
TotalQFrameCK<-as.data.frame(table(as.numeric(CKCDR[1,-1])))
TotalQFrameGK<-as.data.frame(table(as.numeric(GKCDR[1,-1])))
TotalQFrameGKEX<-as.data.frame(bind_rows(table(GKCDR.1921),
table(GKCDR.1922),
table(GKCDR.1923),
table(GKCDR.1924)))
TotalQFrameGKEX[is.na(TotalQFrameGKEX)] <- 0
#exception for 1922 LO 23 & 12
TotalQFrameGKEX
# Cau IV.8
#convert data from problem 6 for reuse
LOW.GK11 <- LOR.GK11
LOW.GK11 <- transform(LOW.GK11, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK11$SoCauTLSai <- abs(LOW.GK11$SoCauTLDung - as.numeric(TotalQFrameGK[1,2]))
LOW.GK11 <- LOW.GK11[,c(1,3)]
#percentaging
LOW.GK11$percent <- round(LOW.GK11[,1]/sum(LOW.GK11[,1]),3)
LOW.GK11
#exception handling for Learning outcome 12
LOW.GK12.1921.3.4<- as.data.frame(bind_rows(DiemGK.1921.LO12,
DiemGK.1923.LO12,
DiemGK.1924.LO12,
))
#fill na with 0
LOW.GK12.1921.3.4[is.na(LOW.GK12.1921.3.4)] <- 0
LOW.GK12.1921.3.4<- as.data.frame(apply(LOW.GK12.1921.3.4,2,sum))
LOW.GK12.1921.3.4$SoCauTLSai <- abs(as.numeric(row.names(LOW.GK12.1921.3.4)) - 12)
#percentaging
LOW.GK12.1921.3.4$percent <-round(LOW.GK12.1921.3.4[,1]/sum(LOW.GK12.1921.3.4[,1]),3)
LOW.GK12.1922<-t(as.data.frame(bind_rows(DiemGK.1922.LO12)))
temp1922 <- abs(as.numeric(row.names(LOW.GK12.1922)) - 11)
LOW.GK12.1922<-as.data.frame(DiemGK.1922.LO12)
LOW.GK12.1922$SoCauTLSai <- as.numeric(temp1922)
LOW.GK12.1922$percent <- round(LOW.GK12.1922[,2]/sum(LOW.GK12.1922[,2]),3)
LOW.GK12.1922<- LOW.GK12.1922[,c(3,4)]
#reserve
DiemGK.1921.LO12.mod <- DiemGK.1921.LO12
DiemGK.1922.LO12.mod <- DiemGK.1922.LO12
DiemGK.1923.LO12.mod <- DiemGK.1923.LO12
DiemGK.1924.LO12.mod <- DiemGK.1924.LO12
rownames(DiemGK.1921.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1921.LO12)) -12)
rownames(DiemGK.1922.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1922.LO12)) -11)
rownames(DiemGK.1923.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1923.LO12)) -12)
rownames(DiemGK.1924.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1924.LO12)) -12)
LOW.GK12 <- as.data.frame(bind_rows(DiemGK.1921.LO12.mod,
DiemGK.1922.LO12.mod,
DiemGK.1923.LO12.mod,
DiemGK.1924.LO12.mod,
))
LOW.GK12[is.na(LOW.GK12)] <- 0
as.numeric(row.names(DiemGK.1921.LO12)) -1
[1] 3 4 5 6 7 8 9 10 11
LOW.GK12<-as.data.frame(apply(LOW.GK12,2,sum))
LOW.GK12
LOW.GK12$SoCauTLSai <- abs(as.numeric(row.names(LOW.GK12)))
LOW.GK12$percent <- round(LOW.GK12[,1]/sum(LOW.GK12[,1]),3)
LOW.GK12
LOW.GK21 <- LOR.GK21
LOW.GK21 <- transform(LOW.GK21, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK21$SoCauTLSai <- abs(LOW.GK21$SoCauTLDung - as.numeric(TotalQFrameGK[3,2]))
LOW.GK21 <- LOW.GK21[,c(1,3)]
LOW.GK21
#percentaging
LOW.GK21$percent <- round(LOW.GK21[,1]/sum(LOW.GK21[,1]),3)
LOW.GK21
LOW.GK22 <- LOR.GK22
LOW.GK22 <- transform(LOW.GK22, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK22$SoCauTLSai <- abs(LOW.GK22$SoCauTLDung - as.numeric(TotalQFrameGK[4,2]))
LOW.GK22 <- LOW.GK22[,c(1,3)]
#percentaging
LOW.GK22$percent <- round(LOW.GK22[,1]/sum(LOW.GK22[,1]),3)
LOW.GK22
LOW.GK31 <- LOR.GK31
LOW.GK31 <- transform(LOW.GK31, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK31$SoCauTLSai <- abs(LOW.GK31$SoCauTLDung - as.numeric(TotalQFrameGK[5,2]))
LOW.GK31 <- LOW.GK31[,c(1,3)]
#percentaging
LOW.GK31$percent <- round(LOW.GK31[,1]/sum(LOW.GK31[,1]),3)
LOW.GK31
#exception for 1922 code
LOW.GK23 <- LOR.GK23
LOW.GK23 <- transform(LOW.GK23, SoCauTraLoiDung = as.numeric(SoCauTraLoiDung))
LOW.GK23$SoCauTLSai <- abs(as.numeric(LOW.GK23$SoCauTraLoiDung) - 2)
LOW.GK23 <- LOW.GK23[,c(2,4)]
#percentaging
LOW.GK23$percent <- round(LOW.GK23[,1]/sum(LOW.GK23[,1]),3)
LOW.GK23
#Graph ---
GLOW.GK11 <- ggplot(LOW.GK11, aes(x = as.character(LOW.GK11[,2]), y = LOW.GK11[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK11[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 11", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK12 <- ggplot(LOW.GK12, aes(x = as.character(LOW.GK12[,2]), y = LOW.GK12[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK12[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 12", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK21 <- ggplot(LOW.GK21, aes(x = as.character(LOW.GK21[,2]), y = LOW.GK21[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK21[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 21", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK22 <- ggplot(LOW.GK22, aes(x = as.character(LOW.GK22[,2]), y = LOW.GK22[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK22[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 22", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK31 <- ggplot(LOW.GK31, aes(x = as.character(LOW.GK31[,2]), y = LOW.GK31[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK31[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK23 <- ggplot(LOW.GK23, aes(x = as.character(LOW.GK23[,2]), y = LOW.GK23[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK23[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK12.1.3.4 <- ggplot(LOW.GK12.1921.3.4, aes(x = as.character(LOW.GK12.1921.3.4[,2]), y = LOW.GK12.1921.3.4[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK12.1921.3.4[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 12-1921-3-4", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK12.2 <- ggplot(LOW.GK12.1922, aes(x = as.character(LOW.GK12.1922[,1]), y = LOW.GK12.1922[,2])) + geom_bar(stat =
"identity") + geom_text(aes(label=LOW.GK12.1922[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 12-1922", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.GK <-ggarrange(GLOW.GK11,GLOW.GK12.2,GLOW.GK12.1.3.4,GLOW.GK12,GLOW.GK21,GLOW.GK22,GLOW.GK23,GLOW.GK31, ncol = 2, nrow = 4)
GLOW.GK

# ggsave("hinh/5481_bieudotansuatcausaigk.jpg", GLOW.GK)
# Cau IV.9
#convert data from problem 6 for reuse
LOW.CK12 <- LOR.CK12
LOW.CK12 <- transform(LOW.CK12, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK12$SoCauTLSai <- abs(LOW.CK12$SoCauTLDung - as.numeric(TotalQFrameCK[1,2]))
LOW.CK12 <- LOW.CK12[,c(1,3)]
#percentaging
LOW.CK12$percent <- round(LOW.CK12[,1]/sum(LOW.CK12[,1]),3)
LOW.CK12
LOW.CK23 <- LOR.CK23
LOW.CK23 <- transform(LOW.CK23, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK23$SoCauTLSai <- abs(LOW.CK23$SoCauTLDung - as.numeric(TotalQFrameCK[2,2]))
LOW.CK23 <- LOW.CK23[,c(1,3)]
#percentaging
LOW.CK23$percent <- round(LOW.CK23[,1]/sum(LOW.CK23[,1]),3)
LOW.CK23
LOW.CK31 <- LOR.CK31
LOW.CK31 <- transform(LOW.CK31, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK31$SoCauTLSai <- abs(LOW.CK31$SoCauTLDung - as.numeric(TotalQFrameCK[3,2]))
LOW.CK31 <- LOW.CK31[,c(1,3)]
#percentaging
LOW.CK31$percent <- round(LOW.CK31[,1]/sum(LOW.CK31[,1]),3)
LOW.CK31
LOW.CK32 <- LOR.CK32
LOW.CK32 <- transform(LOW.CK32, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK32$SoCauTLSai <- abs(LOW.CK32$SoCauTLDung - as.numeric(TotalQFrameCK[4,2]))
LOW.CK32 <- LOW.CK32[,c(1,3)]
#percentaging
LOW.CK32$percent <- round(LOW.CK32[,1]/sum(LOW.CK32[,1]),3)
LOW.CK32
#Graph---
GLOW.CK12 <- ggplot(LOW.CK12, aes(x = as.character(LOW.CK12[,2]), y = LOW.CK12[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK12[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 12", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.CK23 <- ggplot(LOW.CK23, aes(x = as.character(LOW.CK23[,2]), y = LOW.CK23[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK23[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 23", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.CK31 <- ggplot(LOW.CK31, aes(x = as.character(LOW.CK31[,2]), y = LOW.CK31[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK31[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.CK32 <- ggplot(LOW.CK32, aes(x = as.character(LOW.CK32[,2]), y = LOW.CK32[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK32[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 32", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
GLOW.CK <-ggarrange(GLOW.CK12,GLOW.CK23,GLOW.CK31,GLOW.CK32, ncol = 3, nrow = 2)
GLOW.CK

# Cau IV.10
LoListCK <- data.frame(LO= names(table(CKCDR.1922)))
LoListGK <- data.frame(LO= names(table(GKCDR.1922)))
LoAp<-as.data.frame(table(rbind(LoListCK,LoListGK)))
#Lo appeared in both midterm and final
LoAp[LoAp$Freq == 2,]
TotalQFrameCK[TotalQFrameCK$Var1 == 12,]
TotalQFrameGK[TotalQFrameGK$Var1 == 12,]
#Number of students in mid/final
nrow(DiemGK)
nrow(DiemCK)
#create data with 361 students(based on final)
#we will deal with 5 last students later
DiemGKCK<- cbind( DiemGK[1:361,],DiemCK)
#Ma de pool
md <- as.numeric(CKCDR[,1])
#add combined made
DiemGKCK$mdCK_GK <- as.numeric(paste(DiemGKCK[,29] ,DiemGKCK[,62], sep = ""))
#rearrange
DiemGKCK <- DiemGKCK[,c(63,1,4:28,33:61)]
colnames(DiemGKCK)[c(3:56)] <- c(1:54)
#rename column
GKCDR.colrename <-GKCDR
CKCDR.colrename <-CKCDR
colnames(GKCDR.colrename)[2:26] <- c(1:25)
colnames(CKCDR.colrename)[2:30] <- c(26:54)
MD1921.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1921.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1921.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1921.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])
MD1922.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1922.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1922.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1922.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])
MD1923.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1923.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1923.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1923.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])
MD1924.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1924.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1924.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1924.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])
ncol(MD1924.1924)
Diem1921.1921<-DiemGKCK[DiemGKCK[1] == 19211921,]
Diem1921.1922<-DiemGKCK[DiemGKCK[1] == 19211922,]
Diem1921.1923<-DiemGKCK[DiemGKCK[1] == 19211923,]
Diem1921.1924<-DiemGKCK[DiemGKCK[1] == 19211924,]
Diem1922.1921<-DiemGKCK[DiemGKCK[1] == 19221921,]
Diem1922.1922<-DiemGKCK[DiemGKCK[1] == 19221922,]
Diem1922.1923<-DiemGKCK[DiemGKCK[1] == 19221923,]
Diem1922.1924<-DiemGKCK[DiemGKCK[1] == 19221924,]
Diem1923.1921<-DiemGKCK[DiemGKCK[1] == 19231921,]
Diem1923.1922<-DiemGKCK[DiemGKCK[1] == 19231922,]
Diem1923.1923<-DiemGKCK[DiemGKCK[1] == 19231923,]
Diem1923.1924<-DiemGKCK[DiemGKCK[1] == 19231924,]
Diem1924.1921<-DiemGKCK[DiemGKCK[1] == 19241921,]
Diem1924.1922<-DiemGKCK[DiemGKCK[1] == 19241922,]
Diem1924.1923<-DiemGKCK[DiemGKCK[1] == 19241923,]
Diem1924.1924<-DiemGKCK[DiemGKCK[1] == 19241924,]
Diem1921.1921 <- apply(bind_rows(MD1921.1921,Diem1921.1921[,c(3:56)]),2,as.numeric)
Diem1921.1922 <- apply(bind_rows(MD1921.1922,Diem1921.1922[,c(3:56)]),2,as.numeric)
Diem1921.1923 <- apply(bind_rows(MD1921.1923,Diem1921.1923[,c(3:56)]),2,as.numeric)
Diem1921.1924 <- apply(bind_rows(MD1921.1924,Diem1921.1924[,c(3:56)]),2,as.numeric)
Diem1922.1921 <- apply(bind_rows(MD1922.1921,Diem1922.1921[,c(3:56)]),2,as.numeric)
Diem1922.1922 <- apply(bind_rows(MD1922.1922,Diem1922.1922[,c(3:56)]),2,as.numeric)
Diem1922.1923 <- apply(bind_rows(MD1922.1923,Diem1922.1923[,c(3:56)]),2,as.numeric)
Diem1922.1924 <- apply(bind_rows(MD1922.1924,Diem1922.1924[,c(3:56)]),2,as.numeric)
Diem1923.1921 <- apply(bind_rows(MD1923.1921,Diem1923.1921[,c(3:56)]),2,as.numeric)
Diem1923.1922 <- apply(bind_rows(MD1923.1922,Diem1923.1922[,c(3:56)]),2,as.numeric)
Diem1923.1923 <- apply(bind_rows(MD1923.1923,Diem1923.1923[,c(3:56)]),2,as.numeric)
Diem1923.1924 <- apply(bind_rows(MD1923.1924,Diem1923.1924[,c(3:56)]),2,as.numeric)
Diem1924.1921 <- apply(bind_rows(MD1924.1921,Diem1924.1921[,c(3:56)]),2,as.numeric)
Diem1924.1922 <- apply(bind_rows(MD1924.1922,Diem1924.1922[,c(3:56)]),2,as.numeric)
Diem1924.1923 <- apply(bind_rows(MD1924.1923,Diem1924.1923[,c(3:56)]),2,as.numeric)
Diem1924.1924 <- apply(bind_rows(MD1924.1924,Diem1924.1924[,c(3:56)]),2,as.numeric)
#last 5 students did midterm exam but did not do final exam
last5.1922 <- DiemGK[362:366,]
last5.1923 <- DiemGK[362:366,]
last5.1924 <- DiemGK[362:366,]
last5.1922 <- last5.1922 %>% filter(MADE==1922) %>% select(X1:X25) %>% apply(2,as.numeric)
last5.1923 <- last5.1923 %>% filter(MADE==1923) %>% select(X1:X25) %>% apply(2,as.numeric)
last5.1924 <- last5.1924 %>% filter(MADE==1924) %>% select(X1:X25) %>% apply(2,as.numeric)
last5.1922 <- as.data.frame(rbind(GKCDR.1922,last5.1922))
last5.1923 <- as.data.frame(rbind(GKCDR.1923,last5.1923))
last5.1924 <- as.data.frame(rbind(GKCDR.1924,last5.1924))
#Learning outcome 12
LO12.last5.1922 <- table(apply(last5.1922[-1,last5.1922[1,]==12],1,sum))
LO12.last5.1923 <- table(apply(last5.1923[-1,last5.1923[1,]==12],1,sum))
LO12.last5.1924 <- table(apply(last5.1924[-1,last5.1924[1,]==12],1,sum))
rownames(LO12.last5.1922) <- abs(as.numeric(rownames(LO12.last5.1922)) - ncol(GKCDR[GKCDR[,1]==1922,GKCDR[2,] == 12]))
rownames(LO12.last5.1923) <- abs(as.numeric(rownames(LO12.last5.1923)) - ncol(GKCDR[GKCDR[,1]==1923,GKCDR[2,] == 12]))
rownames(LO12.last5.1924) <- abs(as.numeric(rownames(LO12.last5.1924)) - ncol(GKCDR[GKCDR[,1]==1924,GKCDR[2,] == 12]))
LO12.Diem1921.1921 <- table(apply(Diem1921.1921[-1,Diem1921.1921[1,]==12],1,sum))
LO12.Diem1921.1922 <- table(apply(Diem1921.1922[-1,Diem1921.1922[1,]==12],1,sum))
LO12.Diem1921.1923 <- table(apply(Diem1921.1923[-1,Diem1921.1923[1,]==12],1,sum))
LO12.Diem1921.1924 <- table(apply(Diem1921.1924[-1,Diem1921.1924[1,]==12],1,sum))
LO12.Diem1922.1921 <- table(apply(Diem1922.1921[-1,Diem1922.1921[1,]==12],1,sum))
LO12.Diem1922.1922 <- table(apply(Diem1922.1922[-1,Diem1922.1922[1,]==12],1,sum))
LO12.Diem1922.1923 <- table(apply(Diem1922.1923[-1,Diem1922.1923[1,]==12],1,sum))
LO12.Diem1922.1924 <- table(apply(Diem1922.1924[-1,Diem1922.1924[1,]==12],1,sum))
LO12.Diem1923.1921 <- table(apply(Diem1923.1921[-1,Diem1923.1921[1,]==12],1,sum))
LO12.Diem1923.1922 <- table(apply(Diem1923.1922[-1,Diem1923.1922[1,]==12],1,sum))
LO12.Diem1923.1923 <- table(apply(Diem1923.1923[-1,Diem1923.1923[1,]==12],1,sum))
LO12.Diem1923.1924 <- table(apply(Diem1923.1924[-1,Diem1923.1924[1,]==12],1,sum))
LO12.Diem1924.1921 <- table(apply(Diem1924.1921[-1,Diem1924.1921[1,]==12],1,sum))
LO12.Diem1924.1922 <- table(apply(Diem1924.1922[-1,Diem1924.1922[1,]==12],1,sum))
LO12.Diem1924.1923 <- table(apply(Diem1924.1923[-1,Diem1924.1923[1,]==12],1,sum))
LO12.Diem1924.1924 <- table(apply(Diem1924.1924[-1,Diem1924.1924[1,]==12],1,sum))
#make count right answer -> wrong answer
rownames(LO12.Diem1921.1921) <- abs(as.numeric(rownames(LO12.Diem1921.1921)) - ncol(MD1921.1921[,MD1921.1921[1,]==12]))
rownames(LO12.Diem1921.1922) <- abs(as.numeric(rownames(LO12.Diem1921.1922)) - ncol(MD1921.1922[,MD1921.1922[1,]==12]))
rownames(LO12.Diem1921.1923) <- abs(as.numeric(rownames(LO12.Diem1921.1923)) - ncol(MD1921.1923[,MD1921.1923[1,]==12]))
rownames(LO12.Diem1921.1924) <- abs(as.numeric(rownames(LO12.Diem1921.1924)) - ncol(MD1921.1924[,MD1921.1924[1,]==12]))
#different is here
rownames(LO12.Diem1922.1921) <- abs(as.numeric(rownames(LO12.Diem1922.1921)) - ncol(MD1922.1921[,MD1922.1921[1,]==12]))
rownames(LO12.Diem1922.1922) <- abs(as.numeric(rownames(LO12.Diem1922.1922)) - ncol(MD1922.1922[,MD1922.1922[1,]==12]))
rownames(LO12.Diem1922.1923) <- abs(as.numeric(rownames(LO12.Diem1922.1923)) - ncol(MD1922.1923[,MD1922.1923[1,]==12]))
rownames(LO12.Diem1922.1924) <- abs(as.numeric(rownames(LO12.Diem1922.1924)) - ncol(MD1922.1924[,MD1922.1924[1,]==12]))
#-----------------
rownames(LO12.Diem1923.1921) <- abs(as.numeric(rownames(LO12.Diem1923.1921)) - ncol(MD1923.1921[,MD1923.1921[1,]==12]))
rownames(LO12.Diem1923.1922) <- abs(as.numeric(rownames(LO12.Diem1923.1922)) - ncol(MD1923.1922[,MD1923.1922[1,]==12]))
rownames(LO12.Diem1923.1923) <- abs(as.numeric(rownames(LO12.Diem1923.1923)) - ncol(MD1923.1923[,MD1923.1923[1,]==12]))
rownames(LO12.Diem1923.1924) <- abs(as.numeric(rownames(LO12.Diem1923.1924)) - ncol(MD1923.1924[,MD1923.1924[1,]==12]))
rownames(LO12.Diem1924.1921) <- abs(as.numeric(rownames(LO12.Diem1924.1921)) - ncol(MD1924.1921[,MD1924.1921[1,]==12]))
rownames(LO12.Diem1924.1922) <- abs(as.numeric(rownames(LO12.Diem1924.1922)) - ncol(MD1924.1922[,MD1924.1922[1,]==12]))
rownames(LO12.Diem1924.1923) <- abs(as.numeric(rownames(LO12.Diem1924.1923)) - ncol(MD1924.1923[,MD1924.1923[1,]==12]))
rownames(LO12.Diem1924.1924) <- abs(as.numeric(rownames(LO12.Diem1924.1924)) - ncol(MD1924.1924[,MD1924.1924[1,]==12]))
LOW12 <- as.data.frame(bind_rows(LO12.Diem1921.1921,
LO12.Diem1921.1922,
LO12.Diem1921.1923,
LO12.Diem1921.1924,
LO12.Diem1922.1921,
LO12.Diem1922.1922,
LO12.Diem1922.1923,
LO12.Diem1922.1924,
LO12.Diem1923.1921,
LO12.Diem1923.1922,
LO12.Diem1923.1923,
LO12.Diem1923.1924,
LO12.Diem1924.1921,
LO12.Diem1924.1922,
LO12.Diem1924.1923,
LO12.Diem1924.1924,
LO12.last5.1922,
LO12.last5.1923,
LO12.last5.1924
))
LOW12[is.na(LOW12)] <- 0
LOW12 <- as.data.frame(apply(LOW12,2,sum))
LOW12$SoCauTLSai <- as.numeric(row.names(LOW12))
LOW12$percent <- round(LOW12[,1]/sum(LOW12[,1]),3)
#graph----
GLOW12 <- ggplot(LOW12, aes(x = as.character(LOW12[,2]), y = LOW12[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW12[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 12", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
#Learning outcome 23
#expected to be 1
LO23.last5.1922 <- table(last5.1922[-1,last5.1922[1,]==23])
#mini conclude: 1 question related to LO23 on Made1922 was answer by 1 student
#and his/her answer was right -> LO23.last5.1922 = 0.(no need to add to combined)
LO23.Diem1921.1921 <- table(apply(Diem1921.1921[-1,Diem1921.1921[1,]==23],1,sum))
LO23.Diem1921.1922 <- table(apply(Diem1921.1922[-1,Diem1921.1922[1,]==23],1,sum))
LO23.Diem1921.1923 <- table(apply(Diem1921.1923[-1,Diem1921.1923[1,]==23],1,sum))
LO23.Diem1921.1924 <- table(apply(Diem1921.1924[-1,Diem1921.1924[1,]==23],1,sum))
LO23.Diem1922.1921 <- table(apply(Diem1922.1921[-1,Diem1922.1921[1,]==23],1,sum))
LO23.Diem1922.1922 <- table(apply(Diem1922.1922[-1,Diem1922.1922[1,]==23],1,sum))
LO23.Diem1922.1923 <- table(apply(Diem1922.1923[-1,Diem1922.1923[1,]==23],1,sum))
LO23.Diem1922.1924 <- table(apply(Diem1922.1924[-1,Diem1922.1924[1,]==23],1,sum))
LO23.Diem1923.1921 <- table(apply(Diem1923.1921[-1,Diem1923.1921[1,]==23],1,sum))
LO23.Diem1923.1922 <- table(apply(Diem1923.1922[-1,Diem1923.1922[1,]==23],1,sum))
LO23.Diem1923.1923 <- table(apply(Diem1923.1923[-1,Diem1923.1923[1,]==23],1,sum))
LO23.Diem1923.1924 <- table(apply(Diem1923.1924[-1,Diem1923.1924[1,]==23],1,sum))
LO23.Diem1924.1921 <- table(apply(Diem1924.1921[-1,Diem1924.1921[1,]==23],1,sum))
LO23.Diem1924.1922 <- table(apply(Diem1924.1922[-1,Diem1924.1922[1,]==23],1,sum))
LO23.Diem1924.1923 <- table(apply(Diem1924.1923[-1,Diem1924.1923[1,]==23],1,sum))
LO23.Diem1924.1924 <- table(apply(Diem1924.1924[-1,Diem1924.1924[1,]==23],1,sum))
#make count right answer -> wrong answer
rownames(LO23.Diem1921.1921) <- abs(as.numeric(rownames(LO23.Diem1921.1921)) - ncol(MD1921.1921[,MD1921.1921[1,]==23]))
rownames(LO23.Diem1921.1922) <- abs(as.numeric(rownames(LO23.Diem1921.1922)) - ncol(MD1921.1922[,MD1921.1922[1,]==23]))
rownames(LO23.Diem1921.1923) <- abs(as.numeric(rownames(LO23.Diem1921.1923)) - ncol(MD1921.1923[,MD1921.1923[1,]==23]))
rownames(LO23.Diem1921.1924) <- abs(as.numeric(rownames(LO23.Diem1921.1924)) - ncol(MD1921.1924[,MD1921.1924[1,]==23]))
#different is here
rownames(LO23.Diem1922.1921) <- abs(as.numeric(rownames(LO23.Diem1922.1921)) - ncol(MD1922.1921[,MD1922.1921[1,]==23]))
rownames(LO23.Diem1922.1922) <- abs(as.numeric(rownames(LO23.Diem1922.1922)) - ncol(MD1922.1922[,MD1922.1922[1,]==23]))
rownames(LO23.Diem1922.1923) <- abs(as.numeric(rownames(LO23.Diem1922.1923)) - ncol(MD1922.1923[,MD1922.1923[1,]==23]))
rownames(LO23.Diem1922.1924) <- abs(as.numeric(rownames(LO23.Diem1922.1924)) - ncol(MD1922.1924[,MD1922.1924[1,]==23]))
#-----------------
rownames(LO23.Diem1923.1921) <- abs(as.numeric(rownames(LO23.Diem1923.1921)) - ncol(MD1923.1921[,MD1923.1921[1,]==23]))
rownames(LO23.Diem1923.1922) <- abs(as.numeric(rownames(LO23.Diem1923.1922)) - ncol(MD1923.1922[,MD1923.1922[1,]==23]))
rownames(LO23.Diem1923.1923) <- abs(as.numeric(rownames(LO23.Diem1923.1923)) - ncol(MD1923.1923[,MD1923.1923[1,]==23]))
rownames(LO23.Diem1923.1924) <- abs(as.numeric(rownames(LO23.Diem1923.1924)) - ncol(MD1923.1924[,MD1923.1924[1,]==23]))
rownames(LO23.Diem1924.1921) <- abs(as.numeric(rownames(LO23.Diem1924.1921)) - ncol(MD1924.1921[,MD1924.1921[1,]==23]))
rownames(LO23.Diem1924.1922) <- abs(as.numeric(rownames(LO23.Diem1924.1922)) - ncol(MD1924.1922[,MD1924.1922[1,]==23]))
rownames(LO23.Diem1924.1923) <- abs(as.numeric(rownames(LO23.Diem1924.1923)) - ncol(MD1924.1923[,MD1924.1923[1,]==23]))
rownames(LO23.Diem1924.1924) <- abs(as.numeric(rownames(LO23.Diem1924.1924)) - ncol(MD1924.1924[,MD1924.1924[1,]==23]))
LOW23 <- as.data.frame(bind_rows(LO23.Diem1921.1921,
LO23.Diem1921.1922,
LO23.Diem1921.1923,
LO23.Diem1921.1924,
LO23.Diem1922.1921,
LO23.Diem1922.1922,
LO23.Diem1922.1923,
LO23.Diem1922.1924,
LO23.Diem1923.1921,
LO23.Diem1923.1922,
LO23.Diem1923.1923,
LO23.Diem1923.1924,
LO23.Diem1924.1921,
LO23.Diem1924.1922,
LO23.Diem1924.1923,
LO23.Diem1924.1924,
))
LOW23[is.na(LOW23)] <- 0
LOW23 <- as.data.frame(apply(LOW23,2,sum))
LOW23$SoCauTLSai <- as.numeric(row.names(LOW23))
LOW23$percent <- round(LOW23[,1]/sum(LOW23[,1]),3)
#graph----
GLOW23 <- ggplot(LOW23, aes(x = as.character(LOW23[,2]), y = LOW23[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW23[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 23", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
#Learning outcome 31
LO31.last5.1922 <- table(apply(last5.1922[-1,last5.1922[1,]==31],1,sum))
LO31.last5.1923 <- table(apply(last5.1923[-1,last5.1923[1,]==31],1,sum))
LO31.last5.1924 <- table(apply(last5.1924[-1,last5.1924[1,]==31],1,sum))
rownames(LO31.last5.1922) <- abs(as.numeric(rownames(LO31.last5.1922)) - ncol(GKCDR[GKCDR[,1]==1922,GKCDR[2,] == 31]))
rownames(LO31.last5.1923) <- abs(as.numeric(rownames(LO31.last5.1923)) - ncol(GKCDR[GKCDR[,1]==1923,GKCDR[2,] == 31]))
rownames(LO31.last5.1924) <- abs(as.numeric(rownames(LO31.last5.1924)) - ncol(GKCDR[GKCDR[,1]==1924,GKCDR[2,] == 31]))
LO31.Diem1921.1921 <- table(apply(Diem1921.1921[-1,Diem1921.1921[1,]==31],1,sum))
LO31.Diem1921.1922 <- table(apply(Diem1921.1922[-1,Diem1921.1922[1,]==31],1,sum))
LO31.Diem1921.1923 <- table(apply(Diem1921.1923[-1,Diem1921.1923[1,]==31],1,sum))
LO31.Diem1921.1924 <- table(apply(Diem1921.1924[-1,Diem1921.1924[1,]==31],1,sum))
LO31.Diem1922.1921 <- table(apply(Diem1922.1921[-1,Diem1922.1921[1,]==31],1,sum))
LO31.Diem1922.1922 <- table(apply(Diem1922.1922[-1,Diem1922.1922[1,]==31],1,sum))
LO31.Diem1922.1923 <- table(apply(Diem1922.1923[-1,Diem1922.1923[1,]==31],1,sum))
LO31.Diem1922.1924 <- table(apply(Diem1922.1924[-1,Diem1922.1924[1,]==31],1,sum))
LO31.Diem1923.1921 <- table(apply(Diem1923.1921[-1,Diem1923.1921[1,]==31],1,sum))
LO31.Diem1923.1922 <- table(apply(Diem1923.1922[-1,Diem1923.1922[1,]==31],1,sum))
LO31.Diem1923.1923 <- table(apply(Diem1923.1923[-1,Diem1923.1923[1,]==31],1,sum))
LO31.Diem1923.1924 <- table(apply(Diem1923.1924[-1,Diem1923.1924[1,]==31],1,sum))
LO31.Diem1924.1921 <- table(apply(Diem1924.1921[-1,Diem1924.1921[1,]==31],1,sum))
LO31.Diem1924.1922 <- table(apply(Diem1924.1922[-1,Diem1924.1922[1,]==31],1,sum))
LO31.Diem1924.1923 <- table(apply(Diem1924.1923[-1,Diem1924.1923[1,]==31],1,sum))
LO31.Diem1924.1924 <- table(apply(Diem1924.1924[-1,Diem1924.1924[1,]==31],1,sum))
#make count right answer -> wrong answer
rownames(LO31.Diem1921.1921) <- abs(as.numeric(rownames(LO31.Diem1921.1921)) - ncol(MD1921.1921[,MD1921.1921[1,]==31]))
rownames(LO31.Diem1921.1922) <- abs(as.numeric(rownames(LO31.Diem1921.1922)) - ncol(MD1921.1922[,MD1921.1922[1,]==31]))
rownames(LO31.Diem1921.1923) <- abs(as.numeric(rownames(LO31.Diem1921.1923)) - ncol(MD1921.1923[,MD1921.1923[1,]==31]))
rownames(LO31.Diem1921.1924) <- abs(as.numeric(rownames(LO31.Diem1921.1924)) - ncol(MD1921.1924[,MD1921.1924[1,]==31]))
rownames(LO31.Diem1922.1921) <- abs(as.numeric(rownames(LO31.Diem1922.1921)) - ncol(MD1922.1921[,MD1922.1921[1,]==31]))
rownames(LO31.Diem1922.1922) <- abs(as.numeric(rownames(LO31.Diem1922.1922)) - ncol(MD1922.1922[,MD1922.1922[1,]==31]))
rownames(LO31.Diem1922.1923) <- abs(as.numeric(rownames(LO31.Diem1922.1923)) - ncol(MD1922.1923[,MD1922.1923[1,]==31]))
rownames(LO31.Diem1922.1924) <- abs(as.numeric(rownames(LO31.Diem1922.1924)) - ncol(MD1922.1924[,MD1922.1924[1,]==31]))
#-----------------
rownames(LO31.Diem1923.1921) <- abs(as.numeric(rownames(LO31.Diem1923.1921)) - ncol(MD1923.1921[,MD1923.1921[1,]==31]))
rownames(LO31.Diem1923.1922) <- abs(as.numeric(rownames(LO31.Diem1923.1922)) - ncol(MD1923.1922[,MD1923.1922[1,]==31]))
rownames(LO31.Diem1923.1923) <- abs(as.numeric(rownames(LO31.Diem1923.1923)) - ncol(MD1923.1923[,MD1923.1923[1,]==31]))
rownames(LO31.Diem1923.1924) <- abs(as.numeric(rownames(LO31.Diem1923.1924)) - ncol(MD1923.1924[,MD1923.1924[1,]==31]))
rownames(LO31.Diem1924.1921) <- abs(as.numeric(rownames(LO31.Diem1924.1921)) - ncol(MD1924.1921[,MD1924.1921[1,]==31]))
rownames(LO31.Diem1924.1922) <- abs(as.numeric(rownames(LO31.Diem1924.1922)) - ncol(MD1924.1922[,MD1924.1922[1,]==31]))
rownames(LO31.Diem1924.1923) <- abs(as.numeric(rownames(LO31.Diem1924.1923)) - ncol(MD1924.1923[,MD1924.1923[1,]==31]))
rownames(LO31.Diem1924.1924) <- abs(as.numeric(rownames(LO31.Diem1924.1924)) - ncol(MD1924.1924[,MD1924.1924[1,]==31]))
LOW31 <- as.data.frame(bind_rows(LO31.Diem1921.1921,
LO31.Diem1921.1922,
LO31.Diem1921.1923,
LO31.Diem1921.1924,
LO31.Diem1922.1921,
LO31.Diem1922.1922,
LO31.Diem1922.1923,
LO31.Diem1922.1924,
LO31.Diem1923.1921,
LO31.Diem1923.1922,
LO31.Diem1923.1923,
LO31.Diem1923.1924,
LO31.Diem1924.1921,
LO31.Diem1924.1922,
LO31.Diem1924.1923,
LO31.Diem1924.1924,
LO31.last5.1922,
LO31.last5.1923,
LO31.last5.1924
))
LOW31[is.na(LOW31)] <- 0
LOW31 <- as.data.frame(apply(LOW31,2,sum))
LOW31$SoCauTLSai <- as.numeric(row.names(LOW31))
LOW31$percent <- round(LOW31[,1]/sum(LOW31[,1]),3)
GLOW31 <- ggplot(LOW31, aes(x = as.character(LOW31[,2]), y = LOW31[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW31[,3]), vjust=-0.3, size=3.5, )+
labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
#graph----
GW <- ggarrange(GLOW.GK11, GLOW12, GLOW.GK21, GLOW.GK22, GLOW23, GLOW31, GLOW.CK32, ncol=2, nrow = 4)
GW

Phan V
# Cau VI.2
# Gan p = 1
p <- 1
#reuse DiemGK & DiemCK
DiemGKCK.2<- cbind( DiemGK[1:361,],DiemCK)
#add combined made
DiemGKCK.2$mdCK_GK <- as.numeric(paste(DiemGKCK.2[,29] ,DiemGKCK.2[,62], sep = ""))
#rearrange
DiemGKCK.2 <- DiemGKCK.2[,c(63,1:28, 33:61)]
colnames(DiemGKCK.2)[c(5:29)] <- c(1:25)
colnames(DiemGKCK.2)[c(30:58)] <- c(26:54)
Diem2.1921.1921 <- apply(bind_rows(MD1921.1921,DiemGKCK.2[DiemGKCK.2[1] == 19211921,c(2,5:58)]),2,as.numeric)
Diem2.1921.1922 <- apply(bind_rows(MD1921.1922,DiemGKCK.2[DiemGKCK.2[1] == 19211922,c(2,5:58)]),2,as.numeric)
Diem2.1921.1923 <- apply(bind_rows(MD1921.1923,DiemGKCK.2[DiemGKCK.2[1] == 19211923,c(2,5:58)]),2,as.numeric)
Diem2.1921.1924 <- apply(bind_rows(MD1921.1924,DiemGKCK.2[DiemGKCK.2[1] == 19211924,c(2,5:58)]),2,as.numeric)
Diem2.1922.1921 <- apply(bind_rows(MD1922.1921,DiemGKCK.2[DiemGKCK.2[1] == 19221921,c(2,5:58)]),2,as.numeric)
Diem2.1922.1922 <- apply(bind_rows(MD1922.1922,DiemGKCK.2[DiemGKCK.2[1] == 19221922,c(2,5:58)]),2,as.numeric)
Diem2.1922.1923 <- apply(bind_rows(MD1922.1923,DiemGKCK.2[DiemGKCK.2[1] == 19221923,c(2,5:58)]),2,as.numeric)
Diem2.1922.1924 <- apply(bind_rows(MD1922.1924,DiemGKCK.2[DiemGKCK.2[1] == 19221924,c(2,5:58)]),2,as.numeric)
Diem2.1923.1921 <- apply(bind_rows(MD1923.1921,DiemGKCK.2[DiemGKCK.2[1] == 19231921,c(2,5:58)]),2,as.numeric)
Diem2.1923.1922 <- apply(bind_rows(MD1923.1922,DiemGKCK.2[DiemGKCK.2[1] == 19231922,c(2,5:58)]),2,as.numeric)
Diem2.1923.1923 <- apply(bind_rows(MD1923.1923,DiemGKCK.2[DiemGKCK.2[1] == 19231923,c(2,5:58)]),2,as.numeric)
Diem2.1923.1924 <- apply(bind_rows(MD1923.1924,DiemGKCK.2[DiemGKCK.2[1] == 19231924,c(2,5:58)]),2,as.numeric)
Diem2.1924.1921 <- apply(bind_rows(MD1924.1921,DiemGKCK.2[DiemGKCK.2[1] == 19241921,c(2,5:58)]),2,as.numeric)
Diem2.1924.1922 <- apply(bind_rows(MD1924.1922,DiemGKCK.2[DiemGKCK.2[1] == 19241922,c(2,5:58)]),2,as.numeric)
Diem2.1924.1923 <- apply(bind_rows(MD1924.1923,DiemGKCK.2[DiemGKCK.2[1] == 19241923,c(2,5:58)]),2,as.numeric)
Diem2.1924.1924 <- apply(bind_rows(MD1924.1924,DiemGKCK.2[DiemGKCK.2[1] == 19241924,c(2,5:58)]),2,as.numeric)
Diem2.LO12.1921.1921 <- as.data.frame(Diem2.1921.1921[,Diem2.1921.1921[1,] == 12 | is.na(Diem2.1921.1921[1,])])
Diem2.LO12.1921.1922 <- as.data.frame(Diem2.1921.1922[,Diem2.1921.1922[1,] == 12 | is.na(Diem2.1921.1922[1,])])
Diem2.LO12.1921.1923 <- as.data.frame(Diem2.1921.1923[,Diem2.1921.1923[1,] == 12 | is.na(Diem2.1921.1923[1,])])
Diem2.LO12.1921.1924 <- as.data.frame(Diem2.1921.1924[,Diem2.1921.1924[1,] == 12 | is.na(Diem2.1921.1924[1,])])
Diem2.LO12.1922.1921 <- as.data.frame(Diem2.1922.1921[,Diem2.1922.1921[1,] == 12 | is.na(Diem2.1922.1921[1,])])
Diem2.LO12.1922.1922 <- as.data.frame(Diem2.1922.1922[,Diem2.1922.1922[1,] == 12 | is.na(Diem2.1922.1922[1,])])
Diem2.LO12.1922.1923 <- as.data.frame(Diem2.1922.1923[,Diem2.1922.1923[1,] == 12 | is.na(Diem2.1922.1923[1,])])
Diem2.LO12.1922.1924 <- as.data.frame(Diem2.1922.1924[,Diem2.1922.1924[1,] == 12 | is.na(Diem2.1922.1924[1,])])
Diem2.LO12.1923.1921 <- as.data.frame(Diem2.1923.1921[,Diem2.1923.1921[1,] == 12 | is.na(Diem2.1923.1921[1,])])
Diem2.LO12.1923.1922 <- as.data.frame(Diem2.1923.1922[,Diem2.1923.1922[1,] == 12 | is.na(Diem2.1923.1922[1,])])
Diem2.LO12.1923.1923 <- as.data.frame(Diem2.1923.1923[,Diem2.1923.1923[1,] == 12 | is.na(Diem2.1923.1923[1,])])
Diem2.LO12.1923.1924 <- as.data.frame(Diem2.1923.1924[,Diem2.1923.1924[1,] == 12 | is.na(Diem2.1923.1924[1,])])
Diem2.LO12.1924.1921 <- as.data.frame(Diem2.1924.1921[,Diem2.1924.1921[1,] == 12 | is.na(Diem2.1924.1921[1,])])
Diem2.LO12.1924.1922 <- as.data.frame(Diem2.1924.1922[,Diem2.1924.1922[1,] == 12 | is.na(Diem2.1924.1922[1,])])
Diem2.LO12.1924.1923 <- as.data.frame(Diem2.1924.1923[,Diem2.1924.1923[1,] == 12 | is.na(Diem2.1924.1923[1,])])
Diem2.LO12.1924.1924 <- as.data.frame(Diem2.1924.1924[,Diem2.1924.1924[1,] == 12 | is.na(Diem2.1924.1924[1,])])
Diem2.LO12.1921.1921$TLD<- apply(Diem2.LO12.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 12]))],1,sum)
Diem2.LO12.1921.1921[Diem2.LO12.1921.1921[-1,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 12]) - p,"NO"]
NULL
Diem2.LO12.1921.1921$TLD <- apply(Diem2.LO12.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 12]))],1,sum)
Diem2.LO12.1921.1922$TLD <- apply(Diem2.LO12.1921.1922[,c(1:ncol(MD1921.1922[,MD1921.1922 == 12]))],1,sum)
Diem2.LO12.1921.1923$TLD <- apply(Diem2.LO12.1921.1923[,c(1:ncol(MD1921.1923[,MD1921.1923 == 12]))],1,sum)
Diem2.LO12.1921.1924$TLD <- apply(Diem2.LO12.1921.1924[,c(1:ncol(MD1921.1924[,MD1921.1924 == 12]))],1,sum)
Diem2.LO12.1922.1921$TLD <- apply(Diem2.LO12.1922.1921[,c(1:ncol(MD1922.1921[,MD1922.1921 == 12]))],1,sum)
Diem2.LO12.1922.1922$TLD <- apply(Diem2.LO12.1922.1922[,c(1:ncol(MD1922.1922[,MD1922.1922 == 12]))],1,sum)
Diem2.LO12.1922.1923$TLD <- apply(Diem2.LO12.1922.1923[,c(1:ncol(MD1922.1923[,MD1922.1923 == 12]))],1,sum)
Diem2.LO12.1922.1924$TLD <- apply(Diem2.LO12.1922.1924[,c(1:ncol(MD1922.1924[,MD1922.1924 == 12]))],1,sum)
Diem2.LO12.1923.1921$TLD <- apply(Diem2.LO12.1923.1921[,c(1:ncol(MD1923.1921[,MD1923.1921 == 12]))],1,sum)
Diem2.LO12.1923.1922$TLD <- apply(Diem2.LO12.1923.1922[,c(1:ncol(MD1923.1922[,MD1923.1922 == 12]))],1,sum)
Diem2.LO12.1923.1923$TLD <- apply(Diem2.LO12.1923.1923[,c(1:ncol(MD1923.1923[,MD1923.1923 == 12]))],1,sum)
Diem2.LO12.1923.1924$TLD <- apply(Diem2.LO12.1923.1924[,c(1:ncol(MD1923.1924[,MD1923.1924 == 12]))],1,sum)
Diem2.LO12.1924.1921$TLD <- apply(Diem2.LO12.1924.1921[,c(1:ncol(MD1924.1921[,MD1924.1921 == 12]))],1,sum)
Diem2.LO12.1924.1922$TLD <- apply(Diem2.LO12.1924.1922[,c(1:ncol(MD1924.1922[,MD1924.1922 == 12]))],1,sum)
Diem2.LO12.1924.1923$TLD <- apply(Diem2.LO12.1924.1923[,c(1:ncol(MD1924.1923[,MD1924.1923 == 12]))],1,sum)
Diem2.LO12.1924.1924$TLD <- apply(Diem2.LO12.1924.1924[,c(1:ncol(MD1924.1924[,MD1924.1924 == 12]))],1,sum)
#list of student meets learning outcome 12 result requirement
LO12SVList <- ("SV")
LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1921[Diem2.LO12.1921.1921[,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1922[Diem2.LO12.1921.1922[,"TLD"] >= ncol(MD1921.1922[,MD1921.1922 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1923[Diem2.LO12.1921.1923[,"TLD"] >= ncol(MD1921.1923[,MD1921.1923 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1924[Diem2.LO12.1921.1924[,"TLD"] >= ncol(MD1921.1924[,MD1921.1924 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1921[Diem2.LO12.1922.1921[,"TLD"] >= ncol(MD1922.1921[,MD1922.1921 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1922[Diem2.LO12.1922.1922[,"TLD"] >= ncol(MD1922.1922[,MD1922.1922 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1923[Diem2.LO12.1922.1923[,"TLD"] >= ncol(MD1922.1923[,MD1922.1923 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1924[Diem2.LO12.1922.1924[,"TLD"] >= ncol(MD1922.1924[,MD1922.1924 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1921[Diem2.LO12.1923.1921[,"TLD"] >= ncol(MD1923.1921[,MD1923.1921 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1922[Diem2.LO12.1923.1922[,"TLD"] >= ncol(MD1923.1922[,MD1923.1922 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1923[Diem2.LO12.1923.1923[,"TLD"] >= ncol(MD1923.1923[,MD1923.1923 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1924[Diem2.LO12.1923.1924[,"TLD"] >= ncol(MD1923.1924[,MD1923.1924 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1921[Diem2.LO12.1924.1921[,"TLD"] >= ncol(MD1924.1921[,MD1924.1921 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1922[Diem2.LO12.1924.1922[,"TLD"] >= ncol(MD1924.1922[,MD1924.1922 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1923[Diem2.LO12.1924.1923[,"TLD"] >= ncol(MD1924.1923[,MD1924.1923 == 12]) - p,"No" ])
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1924[Diem2.LO12.1924.1924[,"TLD"] >= ncol(MD1924.1924[,MD1924.1924 == 12]) - p,"No" ])
#list of student meets learning outcome 12 result requirement
LO12SVList
[1] "SV" NA NA NA NA "110" NA NA NA NA NA "260"
[13] NA NA NA NA "107" NA NA NA
na.omit(as.data.frame(LO12SVList))
Diem2.LO31.1921.1921 <- as.data.frame(Diem2.1921.1921[,Diem2.1921.1921[1,] == 31 | is.na(Diem2.1921.1921[1,])])
Diem2.LO31.1921.1922 <- as.data.frame(Diem2.1921.1922[,Diem2.1921.1922[1,] == 31 | is.na(Diem2.1921.1922[1,])])
Diem2.LO31.1921.1923 <- as.data.frame(Diem2.1921.1923[,Diem2.1921.1923[1,] == 31 | is.na(Diem2.1921.1923[1,])])
Diem2.LO31.1921.1924 <- as.data.frame(Diem2.1921.1924[,Diem2.1921.1924[1,] == 31 | is.na(Diem2.1921.1924[1,])])
Diem2.LO31.1922.1921 <- as.data.frame(Diem2.1922.1921[,Diem2.1922.1921[1,] == 31 | is.na(Diem2.1922.1921[1,])])
Diem2.LO31.1922.1922 <- as.data.frame(Diem2.1922.1922[,Diem2.1922.1922[1,] == 31 | is.na(Diem2.1922.1922[1,])])
Diem2.LO31.1922.1923 <- as.data.frame(Diem2.1922.1923[,Diem2.1922.1923[1,] == 31 | is.na(Diem2.1922.1923[1,])])
Diem2.LO31.1922.1924 <- as.data.frame(Diem2.1922.1924[,Diem2.1922.1924[1,] == 31 | is.na(Diem2.1922.1924[1,])])
Diem2.LO31.1923.1921 <- as.data.frame(Diem2.1923.1921[,Diem2.1923.1921[1,] == 31 | is.na(Diem2.1923.1921[1,])])
Diem2.LO31.1923.1922 <- as.data.frame(Diem2.1923.1922[,Diem2.1923.1922[1,] == 31 | is.na(Diem2.1923.1922[1,])])
Diem2.LO31.1923.1923 <- as.data.frame(Diem2.1923.1923[,Diem2.1923.1923[1,] == 31 | is.na(Diem2.1923.1923[1,])])
Diem2.LO31.1923.1924 <- as.data.frame(Diem2.1923.1924[,Diem2.1923.1924[1,] == 31 | is.na(Diem2.1923.1924[1,])])
Diem2.LO31.1924.1921 <- as.data.frame(Diem2.1924.1921[,Diem2.1924.1921[1,] == 31 | is.na(Diem2.1924.1921[1,])])
Diem2.LO31.1924.1922 <- as.data.frame(Diem2.1924.1922[,Diem2.1924.1922[1,] == 31 | is.na(Diem2.1924.1922[1,])])
Diem2.LO31.1924.1923 <- as.data.frame(Diem2.1924.1923[,Diem2.1924.1923[1,] == 31 | is.na(Diem2.1924.1923[1,])])
Diem2.LO31.1924.1924 <- as.data.frame(Diem2.1924.1924[,Diem2.1924.1924[1,] == 31 | is.na(Diem2.1924.1924[1,])])
Diem2.LO31.1921.1921$TLD<- apply(Diem2.LO31.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 31]))],1,sum)
Diem2.LO31.1921.1921[Diem2.LO31.1921.1921[-1,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 31]) - p,"NO"]
NULL
Diem2.LO31.1921.1921$TLD <- apply(Diem2.LO31.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 31]))],1,sum)
Diem2.LO31.1921.1922$TLD <- apply(Diem2.LO31.1921.1922[,c(1:ncol(MD1921.1922[,MD1921.1922 == 31]))],1,sum)
Diem2.LO31.1921.1923$TLD <- apply(Diem2.LO31.1921.1923[,c(1:ncol(MD1921.1923[,MD1921.1923 == 31]))],1,sum)
Diem2.LO31.1921.1924$TLD <- apply(Diem2.LO31.1921.1924[,c(1:ncol(MD1921.1924[,MD1921.1924 == 31]))],1,sum)
Diem2.LO31.1922.1921$TLD <- apply(Diem2.LO31.1922.1921[,c(1:ncol(MD1922.1921[,MD1922.1921 == 31]))],1,sum)
Diem2.LO31.1922.1922$TLD <- apply(Diem2.LO31.1922.1922[,c(1:ncol(MD1922.1922[,MD1922.1922 == 31]))],1,sum)
Diem2.LO31.1922.1923$TLD <- apply(Diem2.LO31.1922.1923[,c(1:ncol(MD1922.1923[,MD1922.1923 == 31]))],1,sum)
Diem2.LO31.1922.1924$TLD <- apply(Diem2.LO31.1922.1924[,c(1:ncol(MD1922.1924[,MD1922.1924 == 31]))],1,sum)
Diem2.LO31.1923.1921$TLD <- apply(Diem2.LO31.1923.1921[,c(1:ncol(MD1923.1921[,MD1923.1921 == 31]))],1,sum)
Diem2.LO31.1923.1922$TLD <- apply(Diem2.LO31.1923.1922[,c(1:ncol(MD1923.1922[,MD1923.1922 == 31]))],1,sum)
Diem2.LO31.1923.1923$TLD <- apply(Diem2.LO31.1923.1923[,c(1:ncol(MD1923.1923[,MD1923.1923 == 31]))],1,sum)
Diem2.LO31.1923.1924$TLD <- apply(Diem2.LO31.1923.1924[,c(1:ncol(MD1923.1924[,MD1923.1924 == 31]))],1,sum)
Diem2.LO31.1924.1921$TLD <- apply(Diem2.LO31.1924.1921[,c(1:ncol(MD1924.1921[,MD1924.1921 == 31]))],1,sum)
Diem2.LO31.1924.1922$TLD <- apply(Diem2.LO31.1924.1922[,c(1:ncol(MD1924.1922[,MD1924.1922 == 31]))],1,sum)
Diem2.LO31.1924.1923$TLD <- apply(Diem2.LO31.1924.1923[,c(1:ncol(MD1924.1923[,MD1924.1923 == 31]))],1,sum)
Diem2.LO31.1924.1924$TLD <- apply(Diem2.LO31.1924.1924[,c(1:ncol(MD1924.1924[,MD1924.1924 == 31]))],1,sum)
#list of student meets learning outcome 31 result requirement
LO31SVList <- "SV"
LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1921[Diem2.LO31.1921.1921[,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1922[Diem2.LO31.1921.1922[,"TLD"] >= ncol(MD1921.1922[,MD1921.1922 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1923[Diem2.LO31.1921.1923[,"TLD"] >= ncol(MD1921.1923[,MD1921.1923 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1924[Diem2.LO31.1921.1924[,"TLD"] >= ncol(MD1921.1924[,MD1921.1924 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1921[Diem2.LO31.1922.1921[,"TLD"] >= ncol(MD1922.1921[,MD1922.1921 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1922[Diem2.LO31.1922.1922[,"TLD"] >= ncol(MD1922.1922[,MD1922.1922 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1923[Diem2.LO31.1922.1923[,"TLD"] >= ncol(MD1922.1923[,MD1922.1923 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1924[Diem2.LO31.1922.1924[,"TLD"] >= ncol(MD1922.1924[,MD1922.1924 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1921[Diem2.LO31.1923.1921[,"TLD"] >= ncol(MD1923.1921[,MD1923.1921 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1922[Diem2.LO31.1923.1922[,"TLD"] >= ncol(MD1923.1922[,MD1923.1922 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1923[Diem2.LO31.1923.1923[,"TLD"] >= ncol(MD1923.1923[,MD1923.1923 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1924[Diem2.LO31.1923.1924[,"TLD"] >= ncol(MD1923.1924[,MD1923.1924 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1921[Diem2.LO31.1924.1921[,"TLD"] >= ncol(MD1924.1921[,MD1924.1921 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1922[Diem2.LO31.1924.1922[,"TLD"] >= ncol(MD1924.1922[,MD1924.1922 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1923[Diem2.LO31.1924.1923[,"TLD"] >= ncol(MD1924.1923[,MD1924.1923 == 31]) - p,"No" ])
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1924[Diem2.LO31.1924.1924[,"TLD"] >= ncol(MD1924.1924[,MD1924.1924 == 31]) - p,"No" ])
#list of student meets learning outcome 31 result requirement
LO31SVList
[1] "SV" NA "63" "123" "313" "345" NA "49" "152" "205" "316" NA
[13] "42" "73" "103" "108" "164" "296" "321" "333" NA "60" "71" "91"
[25] "101" "140" "288" "318" NA "11" "20" "25" "70" "80" "159" "209"
[37] "275" "319" NA "17" "43" "48" "59" "67" "104" "116" "147" "274"
[49] "283" "312" "342" "344" "353" NA "90" "93" "225" "323" NA "68"
[61] "83" "87" "120" "189" "251" "310" "320" NA "45" "88" "139" "161"
[73] "252" "260" "299" NA "28" "55" "94" "109" "136" "149" "236" "246"
[85] "271" "354" NA "21" "37" "39" "65" "74" "132" "191" "220" "266"
[97] "273" "311" "341" NA "15" "106" "126" "148" "170" "175" "238" "355"
[109] NA "22" "107" "196" "207" "218" "237" "265" NA "40" "75" "150"
[121] "171" "290" "335" "349" NA "23" "56" "115" "247" "253" "270" "278"
[133] NA "34" "46" "50"
na.omit(as.data.frame(LO31SVList))
#Learning outcome 23 handling exception data of midterm MD1922
#Learning outcome 23 only have 1 question in midterm (MD1922) and 2 in final
TotalQFrameGKEX
#majority of students have right answer for Lo23MD1922 midterm
DiemGK.1922.LO23
DiemGK.1922.LO23
0 1
7 84
#No student have 3 right answer for total Learning outcome 23(mid/final).
LOW23
# Cau VI.3
# IDK
#
GKMN<- DiemGKCK.2[,c(3,5:29)]
Nhom1<-GKMN[GKMN[,1] == "L01",]
Nhom2<-GKMN[GKMN[,1] == "L02",]
Nhom3<-GKMN[GKMN[,1] == "L03",]
Nhom1$TongDiem <-round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1)
#average of each Nhom
mean(round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1))
mean(round(rowSums(apply(Nhom2[,c(2:26)],2,as.numeric))/25*10,1))
mean(round(rowSums(apply(Nhom3[,c(2:26)],2,as.numeric))/25*10,1))
#median of each Nhom
median(round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1))
median(round(rowSums(apply(Nhom2[,c(2:26)],2,as.numeric))/25*10,1))
median(round(rowSums(apply(Nhom3[,c(2:26)],2,as.numeric))/25*10,1))
#Frequency of each Nhom according to Diem
FNhom1 <-as.data.frame(table(round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1)))
colnames(FNhom1) <- c("Diem", "Percentage")
FNhom1$Percentage <- round(FNhom1$Percentage/sum(FNhom1$Percentage)*100,2)
FNhom2 <-as.data.frame(table(round(rowSums(apply(Nhom2[,c(2:26)],2,as.numeric))/25*10,1)))
colnames(FNhom2) <- c("Diem", "Percentage")
FNhom2$Percentage <- round(FNhom2$Percentage/sum(FNhom2$Percentage)*100,2)
FNhom3 <-as.data.frame(table(round(rowSums(apply(Nhom3[,c(2:26)],2,as.numeric))/25*10,1)))
colnames(FNhom3) <- c("Diem", "Percentage")
FNhom3$Percentage <- round(FNhom3$Percentage/sum(FNhom3$Percentage)*100,2)
GNhom1 <- ggplot(FNhom1, aes(x = as.character(FNhom1[,1]), y = FNhom1[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=FNhom1[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Nhom 1", x = "Diem", y = "%SV cua nhom") + theme(panel.background = element_rect(fill = "#d557d5"))
GNhom2 <- ggplot(FNhom2, aes(x = as.character(FNhom2[,1]), y = FNhom2[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=FNhom2[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Nhom 2", x = "Diem", y = "%SV cua nhom") + theme(panel.background = element_rect(fill = "#d557d5"))
GNhom3 <- ggplot(FNhom3, aes(x = as.character(FNhom3[,1]), y = FNhom3[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=FNhom3[,2]), vjust=-0.3, size=3.5, )+
labs(title = "Nhom 3", x = "Diem", y = "%SV cua nhom") + theme(panel.background = element_rect(fill = "#d557d5"))
GNhom <- ggarrange(GNhom1,GNhom2,GNhom3, ncol = 3)
GNhom
#conclusion:
#Nhom3 has high grade with highest average grade
#Nhom1 has low grade with lowest average grade
#Nhom2 has average grade slightly lower than Nhom3
---
title: "Bai tap lon Mon Cau truc roi rac"
output: html_notebook
---

```{r}
# Cai dat cac thu vien can thiet
# install.packages("moments")
# install.packages("dplyr")
# install.packages("readxl")
# install.packages("ggplot2)
# install.packages("hrbrthemes")
```


```{r}
# Load cac thu vien can thiet
library(moments) # Dung de thuc hien cac fucntion Statistics
library(dplyr) # Dung de lam viec voi cac Dataframe
library(readxl) # Dung de doc file Excel
library(xlsx)
library(ggplot2) # Dung de ve do thi
library(gridExtra) # Dung de  dan layout cho cac plot
library(grid)
library(hrbrthemes) # Dung de load theme cho cac plot
library(ggpubr)
```

```{r}
# Tao dataframe Giua ky tu trich xuat file Excel - sheet "GK"
dataGK <- read_excel("192_CO1007.xlsx", sheet = "GK", skip = 4)
dataGK[!complete.cases(dataGK),]

# Tao dataframe Cuoi ky tu trich xuat file Excel - sheet "CK"
dataCK <- read_excel("192_CO1007.xlsx", sheet = "CK", skip = 4)

# Hien thi dataframe Giua ky
dataGK

# Hien thi dataframe Cuoi ky
dataCK
```
```{r}
# Ket qua cham thi Giua ky
KetQuaGK <- dataGK[, c("No", "MANH", "TO", "MADE", "1":"25")]
KetQuaGK

# Bai thi cua Sinh vien Giua ky
# Boi vi trong BaiThiGK co nhung cau Sinh vien bo trong (value = NA), nen chung ta se khong the goi nhu tren
BaiThiGK <- dataGK[, c(1, 2, 3, 29:54)]
BaiThiGK

# Ket qua cham thi Cuoi ky
KetQuaCK <- dataCK[, c("No", "MANH", "TO", "MADE", "1":"29")]
KetQuaCK

# Bai thi cua Sinh vien Cuoi ky
# Tuong tu nhu BaiThiGK
BaiThiCK <- dataCK[, c(1, 2, 3, 33:62)]
BaiThiCK
```

```{r}
# Dung dplyr to cong So cau dung va So cau sai cua tung Sinh vien
# Tao Bang So dap an dung va sai cua sinh vien Giua ky
SoCauDungVaSaiCuaSVGK <- KetQuaGK %>% rowwise() %>%
  mutate(
    SoCauDung = sum(c_across("1":"25")),
    SoCauSai = 25 - sum(c_across("1":"25"))
  )

# Tao Bang So dap an dung va sai cua sinh vien Cuoi ky
SoCauDungVaSaiCuaSVCK <- KetQuaCK %>% rowwise() %>%
  mutate(
    SoCauDung = sum(c_across("1":"29")),
    SoCauSai = 29 - sum(c_across("1":"29"))
  )
```

```{r}
# In ra Tong so cau Dung trong ky thi Giua ky cua sinh vien trong tap mau
TongSoCauDungCuaSVGK = SoCauDungVaSaiCuaSVGK[, c("No", "MANH", "TO", "MADE", "SoCauDung")]
TongSoCauDungCuaSVGK

# In ra Tong so cau Dung trong ky thi Cuoi ky cua sinh vien trong tap mau
TongSoCauDungCuaSVCK = SoCauDungVaSaiCuaSVCK[, c("No", "MANH", "TO", "MADE", "SoCauDung")]
TongSoCauDungCuaSVCK
```

```{r}
# In ra Tong so cau Sai trong ky thi Giua ky cua sinh vien trong tap mau
TongSoCauSaiCuaSVGK = SoCauDungVaSaiCuaSVGK[, c("No", "MANH", "TO", "MADE", "SoCauSai")]
TongSoCauSaiCuaSVGK

# In ra Tong so cau Sai trong ky thi Cuoi ky cua sinh vien trong tap mau
TongSoCauSaiCuaSVCK = SoCauDungVaSaiCuaSVCK[, c("No", "MANH", "TO", "MADE", "SoCauSai")]
TongSoCauSaiCuaSVCK
```


```{r}
# Xac dinh cau dung nhieu nhat va nho nhat trong tap mau Giua ky
# Tinh ra Danh sach (List) so lan Dung cua moi cau Giua ky
SoCauDungGK <- colSums(dataGK[,c(4:28)])
CauDungNhieuNhatGK <- subset(SoCauDungGK, SoCauDungGK == max(SoCauDungGK))
CauDungItNhatGK <- subset(SoCauDungGK,SoCauDungGK == min(SoCauDungGK))
sprintf("Cau %s la cau duoc chon dung nhieu nhat Giua ky voi %s lan chon dung", names(CauDungNhieuNhatGK), CauDungNhieuNhatGK)
sprintf("Cau %s la cau duoc chon dung it nhat Giua ky chi voi %s lan chon dung", names(CauDungItNhatGK), CauDungItNhatGK)

# Xac dinh cau dung nhieu nhat va nho nhat trong tap mau Cuoi ky
# Tinh ra Danh sach (List) so lan Dung cua moi cau Cuoi ky
SoCauDungCK <- colSums(dataCK[,c(4:32)])
CauDungNhieuNhatCK <- subset(SoCauDungCK,SoCauDungCK == max(SoCauDungCK))
CauDungItNhatCK <- subset(SoCauDungCK,SoCauDungCK == min(SoCauDungCK))
sprintf("Cau %s la cau duoc chon dung nhieu nhat Cuoi ky voi %s lan chon dung", names(CauDungNhieuNhatCK), CauDungNhieuNhatCK)
sprintf("Cau %s la cau duoc chon dung it nhat Cuoi ky chi voi %s lan chon dung", names(CauDungItNhatCK), CauDungItNhatCK)
```

```{r fig.height = 10, fig.width = 10}
# Tinh So lan chon dung moi cau theo ma de Giua ky
# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1921
SoCauDungGK1921 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonDungGK1921 <- table(SoCauDungGK1921$SoCauDung)
dDungGK1921 <- data.frame(SoLanChonDungGK1921)
colnames(dDungGK1921) <- c("SoCauDung", "SoSinhVien")
pDungGK1921 <- ggplot(dDungGK1921, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="steelblue") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1922
SoCauDungGK1922 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungGK1922 <- table(SoCauDungGK1922$SoCauDung)
dDungGK1922 <- data.frame(SoLanChonDungGK1922)
colnames(dDungGK1922) <- c("SoCauDung", "SoSinhVien")
pDungGK1922 <- ggplot(dDungGK1922, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="darkgoldenrod1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1923
SoCauDungGK1923 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungGK1923 <- table(SoCauDungGK1923$SoCauDung)
dDungGK1923 <- data.frame(SoLanChonDungGK1923)
colnames(dDungGK1923) <- c("SoCauDung", "SoSinhVien")
pDungGK1923 <- ggplot(dDungGK1923, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="darkseagreen") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauDungCuaSVGK chi gom tung MADE 1924
SoCauDungGK1924 <- subset(TongSoCauDungCuaSVGK, TongSoCauDungCuaSVGK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonDungGK1924 <- table(SoCauDungGK1924$SoCauDung)
dDungGK1924 <- data.frame(SoLanChonDungGK1924)
colnames(dDungGK1924) <- c("SoCauDung", "SoSinhVien")
pDungGK1924 <- ggplot(dDungGK1924, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Hien thi 4 bieu do
g <- grid.arrange(
  arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
  arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
  arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
  arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
  nrow = 2,
  bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Giữa kỳ"
)

# Luu hinh anh vao thu muc hinh
ggsave("hinh/5241_tongsocaudung_gk.jpg", g)
```


```{r fig.height = 10, fig.width = 10}
# Tinh So lan chon dung moi cau theo ma de Cuoi ky
# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1921
SoCauDungCK1921 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonDungCK1921 <- table(SoCauDungCK1921$SoCauDung)
dDungCK1921 <- data.frame(SoLanChonDungCK1921)
colnames(dDungCK1921) <- c("SoCauDung", "SoSinhVien")
pDungCK1921 <- ggplot(dDungCK1921, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1922
SoCauDungCK1922 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungCK1922 <- table(SoCauDungCK1922$SoCauDung)
dDungCK1922 <- data.frame(SoLanChonDungCK1922)
colnames(dDungCK1922) <- c("SoCauDung", "SoSinhVien")
pDungCK1922 <- ggplot(dDungCK1922, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1923
SoCauDungCK1923 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonDungCK1923 <- table(SoCauDungCK1923$SoCauDung)
dDungCK1923 <- data.frame(SoLanChonDungCK1923)
colnames(dDungCK1923) <- c("SoCauDung", "SoSinhVien")
pDungCK1923 <- ggplot(dDungCK1923, aes(x=SoCauDung, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauDungCuaSVCK chi gom tung MADE 1924
SoCauDungCK1924 <- subset(TongSoCauDungCuaSVCK, TongSoCauDungCuaSVCK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonDungCK1924 <- table(SoCauDungCK1924$SoCauDung)
dDungCK1924 <- data.frame(SoLanChonDungCK1924)
colnames(dDungCK1924) <- c("SoCauDung", "SoSinhVien")
pDungCK1924 <- ggplot(dDungCK1924, aes(x=SoCauDung, y=SoSinhVien)) +  geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Hien thi 4 bieu do
g <- grid.arrange(
  arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
  arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
  arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
  arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
  nrow = 2,
  bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Cuối kỳ"
)

# Luu hinh anh vao thu muc hinh
ggsave("hinh/5242_tongsocaudung_ck.jpg", g)
```


```{r fig.height = 10, fig.width = 10}
# Tinh So lan chon sai moi cau theo ma de Giua ky
# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1921
SoCauSaiGK1921 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonSaiGK1921 <- table(SoCauSaiGK1921$SoCauSai)
dSaiGK1921 <- data.frame(SoLanChonSaiGK1921)
colnames(dSaiGK1921) <- c("SoCauSai", "SoSinhVien")
pSaiGK1921 <- ggplot(dSaiGK1921, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1922
SoCauSaiGK1922 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiGK1922 <- table(SoCauSaiGK1922$SoCauSai)
dSaiGK1922 <- data.frame(SoLanChonSaiGK1922)
colnames(dSaiGK1922) <- c("SoCauSai", "SoSinhVien")
pSaiGK1922 <- ggplot(dSaiGK1922, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1923
SoCauSaiGK1923 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiGK1923 <- table(SoCauSaiGK1923$SoCauSai)
dSaiGK1923 <- data.frame(SoLanChonSaiGK1923)
colnames(dSaiGK1923) <- c("SoCauSai", "SoSinhVien")
pSaiGK1923 <- ggplot(dSaiGK1923, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauSaiCuaSVGK chi gom tung MADE 1924
SoCauSaiGK1924 <- subset(TongSoCauSaiCuaSVGK, TongSoCauSaiCuaSVGK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonSaiGK1924 <- table(SoCauSaiGK1924$SoCauSai)
dSaiGK1924 <- data.frame(SoLanChonSaiGK1924)
colnames(dSaiGK1924) <- c("SoCauSai", "SoSinhVien")
pSaiGK1924 <- ggplot(dSaiGK1924, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Hien thi 4 bieu do
g <- grid.arrange(
  arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
  arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
  arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
  arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
  nrow = 2,
  bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Giữa kỳ"
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5243_tongsocausai_gk.jpg", g)
```
```{r fig.height = 10, fig.width = 10}
# Tinh So lan chon sai moi cau theo ma de Cuoi ky
# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1921
SoCauSaiCK1921 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1921)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1921
SoLanChonSaiCK1921 <- table(SoCauSaiCK1921$SoCauSai)
dSaiCK1921 <- data.frame(SoLanChonSaiCK1921)
colnames(dSaiCK1921) <- c("SoCauSai", "SoSinhVien")
pSaiCK1921 <- ggplot(dSaiCK1921, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1922
SoCauSaiCK1922 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1922)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiCK1922 <- table(SoCauSaiCK1922$SoCauSai)
dSaiCK1922 <- data.frame(SoLanChonSaiCK1922)
colnames(dSaiCK1922) <- c("SoCauSai", "SoSinhVien")
pSaiCK1922 <- ggplot(dSaiCK1922, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1923
SoCauSaiCK1923 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1923)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1922
SoLanChonSaiCK1923 <- table(SoCauSaiCK1923$SoCauSai)
dSaiCK1923 <- data.frame(SoLanChonSaiCK1923)
colnames(dSaiCK1923) <- c("SoCauSai", "SoSinhVien")
pSaiCK1923 <- ggplot(dSaiCK1923, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Tao subset tu TongSoCauSaiCuaSVCK chi gom tung MADE 1924
SoCauSaiCK1924 <- subset(TongSoCauSaiCuaSVCK, TongSoCauSaiCuaSVCK$MADE == 1924)
# Tao matrix 1 x n trong do n la so cau chon dung cua sinh vien the ma de 1924
SoLanChonSaiCK1924 <- table(SoCauSaiCK1924$SoCauSai)
dSaiCK1924 <- data.frame(SoLanChonSaiCK1924)
colnames(dSaiCK1924) <- c("SoCauSai", "SoSinhVien")
pSaiCK1924 <- ggplot(dSaiCK1924, aes(x=SoCauSai, y=SoSinhVien)) + geom_bar(stat="identity", fill="coral1") + geom_text(aes(label = SoSinhVien, y = SoSinhVien), size = 3.5, vjust = 1.6)

# Hien thi 4 bieu do
g <- grid.arrange(
  arrangeGrob(pDungGK1921, bottom = "Đề 1921"),
  arrangeGrob(pDungGK1922, bottom = "Đề 1922"),
  arrangeGrob(pDungGK1923, bottom = "Đề 1923"),
  arrangeGrob(pDungGK1924, bottom = "Đề 1924"),
  nrow = 2,
  bottom = "Tổng số các câu đúng của sinh viên theo Mã đề của tập mẫu Cuối kỳ"
)

# Luu hinh anh vao thu muc hinh
ggsave("hinh/5244_tongsocausai_ck.jpg", g)
```
Phan III
```{r}
# Tinh diem cua moi sinh vien trong ky thi Giua ky va Cuoi ky
dataGK$TongDiemGK = (round(rowSums(dataGK[,c(4:28)])/25*10,1))
dataCK$TongDiemCK = (round(rowSums(dataCK[,c(4:32)])/29*10,1))

# Tao ra 1 table moi chi gom 2 cot No & TongDiemGK/TongDiemCK
DiemCuaMoiSVGK<- select(dataGK,No,TongDiemGK)
DiemCuaMoiSVCK<- select(dataCK,No,TongDiemCK)

# Theme cho table
thm <- ttheme_minimal(
  core=list(bg_params = list(fill = blues9[1:4], col=NA),
            fg_params=list(fontface=3)),
  colhead=list(fg_params=list(col="navyblue", fontface=4L)),
  rowhead=list(fg_params=list(col="orange", fontface=3L)))

# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
  tableGrob(head(DiemCuaMoiSVGK, 10), theme = thm),
  tableGrob(head(DiemCuaMoiSVCK, 10), theme = thm),
  ncol=2
)
# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5311_diem.jpg", g)
```
```{r}
# Tao 1 bang moi bang cach join 2 bang DiemCuaMoiSVCK & DiemCuaMoiSVGK
DiemTongKet<-full_join(DiemCuaMoiSVCK,DiemCuaMoiSVGK)
DiemTongKet$TongKet = round(((rowSums(DiemTongKet[,c(2:2)])*0.6 +
                                rowSums(DiemTongKet[,c(3:3)])*0.4)),1)
DiemTongKet

# Theme cho table
thm <- ttheme_minimal(
  core=list(bg_params = list(fill = blues9[1:4], col=NA),
            fg_params=list(fontface=3)),
  colhead=list(fg_params=list(col="navyblue", fontface=4L)),
  rowhead=list(fg_params=list(col="orange", fontface=3L)))

# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
  tableGrob(head(DiemTongKet, 10), theme = thm),
  ncol=1
)
# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5321_diem.jpg", g)
```

```{r}
# Tinh Trung vi mau, Cuc dai mau, Cuc tieu mau cua ky thi Giua ky
TrungViMauGK <- round(median(dataGK$TongDiemGK), 2)
CucDaiMauGK <- round(max(dataGK$TongDiemGK), 2)
CucTieuMauGK <- round(min(dataGK$TongDiemGK), 2)

# Tinh Trung vi mau, Cuc dai mau, Cuc tieu mau cua ky thi Giua ky
TrungViMauCK <- round(median(dataCK$TongDiemCK), 2)
CucDaiMauCK <- round(max(dataCK$TongDiemCK), 2)
CucTieuMauCK <- round(min(dataCK$TongDiemCK), 2)

# Tao 1 dataframe de plot table
df <- data.frame(
  row.names = c("Giua ky", "Cuoi ky"),
  TrungViMau = c(TrungViMauGK, TrungViMauCK),
  CucDaiMau = c(CucDaiMauGK, CucDaiMauCK),
  CucTieuMau = c(CucTieuMauGK, CucTieuMauCK)
)

# Theme cho table
thm <- ttheme_minimal(
  core=list(bg_params = list(fill = blues9[1:4], col=NA),
            fg_params=list(fontface=3)),
  colhead=list(fg_params=list(col="navyblue", fontface=4L)),
  rowhead=list(fg_params=list(col="orange", fontface=3L)))

# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
  tableGrob(df, theme = thm),
  ncol=1
)
# Luu hinh anh vao thu muc hinh
ggsave("hinh/5311_medianmaxmin.jpg", g)
```

```{r}
# Dung subset de loai cac gia tri co N/A
SinhVienCoDiemGKLonHonBang9 <- nrow(subset(DiemTongKet, TongDiemGK >= 9))
SinhVienCoDiemCKLonHonBang9 <- nrow(subset(DiemTongKet, TongDiemCK >= 9))
sprintf("So sinh vien co diem Giua ky lon hon hoac bang 9 la %s", SinhVienCoDiemGKLonHonBang9)
sprintf("So sinh vien co diem Cuoi ky lon hon hoac bang 9 la %s", SinhVienCoDiemCKLonHonBang9)
```

```{r}
SinhVienCoDiemGKLonHonBang7 <- nrow(subset(DiemTongKet, TongDiemGK >= 7))
SinhVienCoDiemCKLonHonBang7 <- nrow(subset(DiemTongKet, TongDiemCK >= 7))
sprintf("So sinh vien co diem Giua ky lon hon hoac bang 7 la %s", SinhVienCoDiemGKLonHonBang7)
sprintf("So sinh vien co diem Cuoi ky lon hon hoac bang 7 la %s", SinhVienCoDiemCKLonHonBang7)
```

```{r}
SinhVienCoDiemGKLonHonBang5 <- nrow(subset(DiemTongKet, TongDiemGK >= 5))
SinhVienCoDiemCKLonHonBang5 <- nrow(subset(DiemTongKet, TongDiemCK >= 5))
sprintf("So sinh vien co diem Giua ky lon hon hoac bang 5 la %s", SinhVienCoDiemGKLonHonBang5)
sprintf("So sinh vien co diem Cuoi ky lon hon hoac bang 5 la %s", SinhVienCoDiemCKLonHonBang5)
```

```{r}
SinhVienCoDiemGKNhoHon5 <-nrow(subset(DiemTongKet, TongDiemGK < 5))
SinhVienCoDiemCKNhoHon5 <- nrow(subset(DiemTongKet, TongDiemCK < 5))
sprintf("So sinh vien co diem Giua ky nho hon 5 la %s", SinhVienCoDiemGKNhoHon5)
sprintf("So sinh vien co diem Cuoi ky nho hon 5 la %s", SinhVienCoDiemCKNhoHon5)
```


```{r fig.height = 10, fig.width = 10}
# Plot diem Giua ky
# Tao 1  table moi dua tren col TongDiemGK de dem so occurrence cua moi diem Giua ky
dfDemDiemGK <- data.frame(table(dataGK$TongDiemGK))
colnames(dfDemDiemGK) <- c("DiemGK", "SoSV")
plotDiemGK <- ggplot(dfDemDiemGK, aes(x=DiemGK, y=SoSV)) +
  geom_line(color="grey", aes(group=1)) +
  geom_point(shape=21, color="black", fill="steelblue", size=6) + theme_ipsum() +
  xlab("Diem") + ylab("So Sinh vien") +
  theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
  geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))

# Plot diem Cuoi ky
# Tao 1  table moi dua tren col TongDiemCK de dem so occurrence cua moi diem Cuoi ky
dfDemDiemCK <- data.frame(table(dataCK$TongDiemCK))
colnames(dfDemDiemCK) <- c("DiemCK", "SoSV")
plotDiemCK <- ggplot(dfDemDiemCK, aes(x=DiemCK, y=SoSV)) +
  geom_line(color="grey", aes(group=1)) +
  geom_point(shape=21, color="black", fill="coral1", size=6) + theme_ipsum() +
  xlab("Diem") + ylab("So Sinh vien") +
  theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
  geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))

# Hien thi 2 bieu do
g <- grid.arrange(
  arrangeGrob(plotDiemGK, bottom = "Diem Giua ky"),
  arrangeGrob(plotDiemCK, bottom = "Diem Cuoi ky"),
  nrow = 2, ncol = 1
)
# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5361_plotphodiemsinhvien.jpg", g)

```
```{r}
# Tao 1 table moi tu dataGK voi tat ca cac column va voi cac row co TongDiemGK la cao nhat
SVDiemCaoNhatGK<- select(dataGK[dataGK[,"TongDiemGK"] == max(dataGK$TongDiemGK),], No, MANH, TO,TongDiemGK)

# Tao 1 table moi tu dataCK voi tat ca cac column va voi cac row co TongDiemCK la cao nhat
SVDiemCaoNhatCK<- select(dataCK[dataCK[,"TongDiemCK"] == max(dataCK$TongDiemCK),], No, MANH, TO,TongDiemCK)

# Theme cho table
thm <- ttheme_minimal(
  core=list(bg_params = list(fill = blues9[1:4], col=NA),
            fg_params=list(fontface=3)),
  colhead=list(fg_params=list(col="navyblue", fontface=4L)),
  rowhead=list(fg_params=list(col="orange", fontface=3L)))

# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
  tableGrob(head(SVDiemCaoNhatGK, 10,), theme = thm),
  tableGrob(head(SVDiemCaoNhatCK, 10), theme = thm),
  ncol=2, nrow=1
)
# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5371_diemcaonhat.jpg", g)
```

```{r}
# Tao 1 table moi tu dataGK voi tat ca cac column va voi cac row co TongDiemGK la nho nhat
SVDiemNhoNhatGK<- select(dataGK[dataGK[,"TongDiemGK"] == min(dataGK$TongDiemGK),], No, MANH, TO,TongDiemGK)

# Tao 1 table moi tu dataGK voi tat ca cac column va voi cac row co TongDiemGK la nho nhat
SVDiemNhoNhatCK<- select(dataCK[dataCK[,"TongDiemCK"] == min(dataCK$TongDiemCK),], No, MANH, TO,TongDiemCK)

# Theme cho table
thm <- ttheme_minimal(
  core=list(bg_params = list(fill = blues9[1:4], col=NA),
            fg_params=list(fontface=3)),
  colhead=list(fg_params=list(col="navyblue", fontface=4L)),
  rowhead=list(fg_params=list(col="orange", fontface=3L)))

# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
  tableGrob(head(SVDiemNhoNhatGK, 10,), theme = thm),
  tableGrob(head(SVDiemNhoNhatCK, 10), theme = thm),
  ncol=2, nrow=1
)
# Luu hinh anh vao thu muc hinh
# ggsave("hinh/5381_diemthapnhat.jpg", g)
```

```{r}
DiemTrungBinhGK <- round(mean(DiemCuaMoiSVGK$TongDiemGK),1)
DiemTrungBinhCK <- round(mean(DiemCuaMoiSVCK$TongDiemCK),1)
sprintf("Diem trung binh cua cac sinh vien trong tap mau Giua ky la %s", DiemTrungBinhGK)
sprintf("Diem trung binh cua cac sinh vien trong tap mau Cuoi ky la %s", DiemTrungBinhCK)
```

```{r}
SVCoDiemTrungBinhGK <-nrow(select(dataGK[dataGK[,"TongDiemGK"] == DiemTrungBinhGK,], No, MANH, TO,TongDiemGK))
SVCoDiemTrungBinhCK <-nrow(select(dataCK[dataCK[,"TongDiemCK"] == DiemTrungBinhCK,], No, MANH, TO,TongDiemCK))
sprintf("So sinh vien co diem trung binh %s trong tap mau Giua ky la %s", DiemTrungBinhGK, SVCoDiemTrungBinhGK)
sprintf("So sinh vien co diem trung binh %s trong tap mau Cuoi ky la %s", DiemTrungBinhCK, SVCoDiemTrungBinhCK)
```

```{r}
# Xac dinh do phan tan cua diem so chinh la Standard Deviation
DoPhanTanGK <- sd(DiemCuaMoiSVGK$TongDiemGK)
DoPhanTanCK <- sd(DiemCuaMoiSVCK$TongDiemCK)

# Tao dataframe de chuan bi plot bieu do
dfPhanTan <- data.frame(
  KyThi = c("Giua ky", "Cuoi ky"),
  DiemTrungBinh = c(DiemTrungBinhGK, DiemTrungBinhCK),
  DoPhanTan = c(DoPhanTanGK, DoPhanTanCK)
)
g <- ggplot(dfPhanTan, aes(x=KyThi, y=DiemTrungBinh)) +
  geom_errorbar(aes(ymin=round(DiemTrungBinh - DoPhanTan, 2), ymax=round(DiemTrungBinh + DoPhanTan, 2)), width=0.2, color="coral1") +
  geom_text(aes(label = round(DiemTrungBinh - DoPhanTan, 2), y = round(DiemTrungBinh - DoPhanTan, 2)), vjust = 1.5) +
  geom_text(aes(label = round(DiemTrungBinh + DoPhanTan, 2), y = round(DiemTrungBinh + DoPhanTan, 2)), vjust = -.5) +
  geom_point(shape=21, color="black", fill="steelblue", size=6) +
  geom_text(aes(label=round(DoPhanTan, 2)),hjust=1.5, vjust=0, color="coral1") +
  labs(y="Muc do phan tan cua diem so", x="Ky thi")
# Luu hinh anh vao thu muc hinh
# ggsave("hinh/53111_mucdophantan.jpg", g)
```


```{r}
# Cau III.12
# Tinh do meo lech cua du lieu bang ham skewness
skewnessGK <- skewness(DiemCuaMoiSVGK$TongDiemGK)
skewnessCK <- skewness(DiemCuaMoiSVCK$TongDiemCK)
sprintf("Do meo lech (skewness) cua du lieu trong mau Giua ky la %s", skewnessGK)
sprintf("Do meo lech (skewness) cua du lieu trong mau Cuoi ky la %s", skewnessCK)

# Tinh do nhon cua du lieu bang ham kurtosis
kurtosisGK <- kurtosis(DiemCuaMoiSVGK$TongDiemGK)
kurtosisCK <- kurtosis(DiemCuaMoiSVCK$TongDiemCK)
sprintf("Do nhon (kurtosis) cua du lieu trong mau Giua ky la %s", kurtosisGK)
sprintf("Do nhon (kurtosis) cua du lieu trong mau Cuoi ky la %s", kurtosisCK)
```

```{r}
# Cau III.13
# Giat thich Q1 va Q3
# 0% -> 25% -> 50% -> 75% -> 100%
# min -> Q1 -> mean -> Q3 -> max
# Tinh Q1 & Q3 cho tap mau Giua ky
Q1GK = quantile(DiemCuaMoiSVGK$TongDiemGK,0.25)
Q3GK = quantile(DiemCuaMoiSVGK$TongDiemGK,0.75)
sprintf("Tu phan vi (quartile) thu nhat (Q1) va thu ba (Q3) cua tap mau Giua ky lan luot la %s va %s", Q1GK, Q3GK)

# Tinh Q1 & Q3 cho tap mau Cuoi ky
Q1CK = quantile(DiemCuaMoiSVCK$TongDiemCK,0.25)
Q3CK = quantile(DiemCuaMoiSVCK$TongDiemCK,0.75)
sprintf("Tu phan vi (quartile) thu nhat (Q1) va thu ba (Q3) cua tap mau Cuoi ky lan luot la %s va %s", Q1CK, Q3CK)
```

```{r}
# Cau III.14
# Tao ham tinh ra table chua ca sinh vien co muc diem Giua ky cao thu K
GKK <- function(k){
  temp<-table(round(TongSoCauDungCuaSVGK$SoCauDung/25*10,1))
  options(digits = 2)
  muc <- as.double(names(temp[length(temp)- k + 1]))
  DS <- dataGK[dataGK[,"TongDiemGK"] == muc,]
  return(DS)
}

# Tao ham tinh ra table chua ca sinh vien co muc diem Cuoi ky cao thu K
CKK <- function(k){
  temp<-table(round(TongSoCauDungCuaSVCK$SoCauDung/29*10,1))
  options(digits = 2)
  muc <- as.double(names(temp[length(temp)- k + 1]))
  DS <- dataCK[dataCK[,"TongDiemCK"] == muc,]
  return(DS)
}

# Tao table voi tat ca column va lay nhung row co TongDiemGK o muc diem cao nhat trong tap mau Giua ky va Cuoi ky
DSSVQ3GK <- rbind(GKK(1), GKK(2))
DSSVQ3CK <- rbind(CKK(1), CKK(2))

# So luong sinh vien chinh la so dong (row) cua table moi tao
SoSVQ3GK = nrow(DSSVQ3GK)
SoSVQ3CK = nrow(DSSVQ3CK)
sprintf("So sinh vien co diem so nam trong 2 muc diem cao nhat trong tap Giua ky la %s", SoSVQ3GK)
sprintf("So sinh vien co diem so nam trong 2 muc diem cao nhat trong tap Cuoi ky la %s", SoSVQ3CK)
```

```{r fig.height = 10, fig.width = 10}
# Cau III.15
# Plot diem Giua ky
# Tao 1  table moi dua tren col TongDiemGK de dem so occurrence cua moi diem lon hon Q3 cua tap mau Giua ky
dfDemDiemQ3GK <- data.frame(table(DSSVQ3GK$TongDiemGK))
colnames(dfDemDiemQ3GK) <- c("DiemGK", "SoSV")
plotDiemQ3GK <- ggplot(dfDemDiemQ3GK, aes(x=DiemGK, y=SoSV)) +
  geom_line(color="grey", aes(group=1)) +
  geom_point(shape=21, color="black", fill="steelblue", size=6) + theme_ipsum() +
  xlab("Diem") + ylab("So Sinh vien") +
  theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
  geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))

# Plot diem cuoi ky
# Tao 1  table moi dua tren col TongDiemCK de dem so occurrence cua moi diem lon hon Q3 cua tap mau Cuoi ky
dfDemDiemQ3CK <- data.frame(table(DSSVQ3CK$TongDiemCK))
colnames(dfDemDiemQ3CK) <- c("DiemCK", "SoSV")
plotDiemQ3CK <- ggplot(dfDemDiemQ3CK, aes(x=DiemCK, y=SoSV)) +
  geom_line(color="grey", aes(group=1)) +
  geom_point(shape=21, color="black", fill="coral1", size=6) + theme_ipsum() +
  xlab("Diem") + ylab("So Sinh vien") +
  theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
  geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))

# Hien thi 2 bieu do
g <- grid.arrange(
  arrangeGrob(plotDiemQ3GK, bottom = "Diem Giua ky"),
  arrangeGrob(plotDiemQ3CK, bottom = "Diem Cuoi ky"),
  nrow = 2, ncol = 1
)
g
# Luu hinh anh vao thu muc hinh
ggsave("hinh/53151_plotphodiemsinhvienQ3.jpg", g)
```
```{r fig.height = 10, fig.width = 10}
# Cau III.16
# Tan dung lai 2 ham GKK & CKK de tinh so sinh vien co muc diem cao thu K
# Dat k = 5
k = 5
# Tao table voi tat ca column va lay nhung row co TongDiemGK o muc diem cao thu k trong tap mau Giua ky va Cuoi ky
DSSV_K_GK <- GKK(k) 
DSSV_K_CK <- CKK(k)

# So luong sinh vien chinh la so dong (row) cua table moi tao
SoSV_K_GK = nrow(DSSV_K_GK)
SoSV_K_CK = nrow(DSSV_K_CK)
sprintf("So sinh vien co diem so voi muc diem cao thu %s trong tap Giua ky la %s", k, SoSV_K_GK)
sprintf("So sinh vien co diem so voi muc diem cao thu %s trong tap Cuoi ky la %s", k, SoSVQ3CK)
```
```{r fig.height = 10, fig.width = 10}
# Cau III.17
# Plot diem Giua ky
# Tao 1  table moi dua tren col TongDiemGK de dem so occurrence cua moi diem lon hon Q3 cua tap mau Giua ky
plotDemDiemK <- function(kythi, dssv) {
  if (kythi == "GK") {
    dfDemDiemK <- data.frame(table(dssv$TongDiemGK))
    colnames(dfDemDiemK) <- c("DiemGK", "SoSV")
    return(
      ggplot(dfDemDiemK, aes(x=DiemGK, y=SoSV)) +
        geom_line(color="grey", aes(group=1)) +
        geom_point(shape=21, color="black", fill="steelblue", size=6) + theme_ipsum() +
        xlab("Diem") + ylab("So Sinh vien") +
        theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
        geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
    )
  } else if (kythi == "CK") {
    dfDemDiemK <- data.frame(table(dssv$TongDiemCK))
    colnames(dfDemDiemK) <- c("DiemCK", "SoSV")
    return(
      ggplot(dfDemDiemK, aes(x=DiemCK, y=SoSV)) +
        geom_line(color="grey", aes(group=1)) +
        geom_point(shape=21, color="black", fill="coral1", size=6) + theme_ipsum() +
        xlab("Diem") + ylab("So Sinh vien") +
        theme(panel.background=element_rect(fill="cornsilk", colour=NA)) +
        geom_text(aes(label=SoSV, hjust=-1.5, vjust=0))
    )
  }
}

# Chon 3 K lan luoi bang 5, 3, 9 cho ca 2 tap mau Giua ky va Cuoi ky
dssv_k5_GK <- GKK(5)
dssv_k3_GK <- GKK(3)
dssv_k9_GK <- GKK(9)
dssv_GK <- rbind(dssv_k3_GK, dssv_k5_GK, dssv_k9_GK)
plotDiem_K_GK <- plotDemDiemK("GK", dssv_GK)

dssv_k5_CK <- CKK(5)
dssv_k3_CK <- CKK(3)
dssv_k9_CK <- CKK(9)
dssv_CK <- rbind(dssv_k3_CK, dssv_k5_CK, dssv_k9_CK)
plotDiem_K_CK <- plotDemDiemK("CK", dssv_CK)

# Ve table chua K
# Theme cho table
thm <- ttheme_minimal(
  core=list(bg_params = list(fill = blues9[1:4], col=NA),
            fg_params=list(fontface=3)),
  colhead=list(fg_params=list(col="navyblue", fontface=4L)),
  rowhead=list(fg_params=list(col="orange", fontface=3L)))

dfTableK <- data.frame(
  k = c("3", "5", "9"),
  DiemGK = c(dssv_k3_GK$TongDiemGK[[1]], dssv_k5_GK$TongDiemGK[[1]], dssv_k9_GK$TongDiemGK[[1]]),
  DiemCK = c(dssv_k3_CK$TongDiemCK[[1]], dssv_k5_CK$TongDiemCK[[1]], dssv_k9_CK$TongDiemCK[[1]])
)
# Dan 2 table diem Giua ky va Cuoi ky
g <- grid.arrange(
  tableGrob(dfTableK, theme = thm),
  ncol=1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/53171_thongtink.jpg", g)

# Hien thi 2 bieu do
grid.newpage()
g <- grid.arrange(
  arrangeGrob(plotDiem_K_GK, bottom = "Diem Giua ky"),
  arrangeGrob(plotDiem_K_CK, bottom = "Diem Cuoi ky"),
  nrow = 2, ncol = 1
)

# Luu hinh anh vao thu muc hinh
# ggsave("hinh/53172_plotphodiemsinhvienK.jpg", g)
```

Phan IV
```{r}
# Import du lieu thi Giua ky
DFGK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 3, startRow = 1, endRow = 16)

# Import du lieu thi Cuoi ky
DFCK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 5, startRow = 1, endRow = 16)
```

```{r}
# Xac dinh cac chuan dau ra xuat hien trong tap mau Giua ky
GKCDR <- DFGK[7:10, 1:26]
# Tao ra 1 matrix co chieu dai 1 x n (voi n la column tu X1 - X25)
GKCDR.1921 <- GKCDR %>% filter(GKCDR[1] == "1921") %>% select(X1:X25) %>% apply(2,as.numeric)
GKCDR.1922 <- GKCDR %>% filter(GKCDR[1] == "1922") %>% select(X1:X25) %>% apply(2,as.numeric)
GKCDR.1923 <- GKCDR %>% filter(GKCDR[1] == "1923") %>% select(X1:X25) %>% apply(2,as.numeric)
GKCDR.1924 <- GKCDR %>% filter(GKCDR[1] == "1924") %>% select(X1:X25) %>% apply(2,as.numeric)

# Xac dinh cac chuan dau ra xuat hien trong tap mau Cuoi ky
CKCDR <- DFCK[7:10, 1:30]
# Tao ra 1 matrix co chieu dai 1 x n (voi n la column tu X1 - X29)
CKCDR.1921 <- CKCDR %>% filter(CKCDR[1] == "1921") %>% select(X1:X29) %>% apply(2,as.numeric)
CKCDR.1922 <- CKCDR %>% filter(CKCDR[1] == "1922") %>% select(X1:X29) %>% apply(2,as.numeric)
CKCDR.1923 <- CKCDR %>% filter(CKCDR[1] == "1923") %>% select(X1:X29) %>% apply(2,as.numeric)
CKCDR.1924 <- CKCDR %>% filter(CKCDR[1] == "1924") %>% select(X1:X29) %>% apply(2,as.numeric)
```


```{r}
# Cau IV.1
# table(GKCDR.1921) se group cac chuan dau ra lai cung voi count cua tung chuan dau ra.
# So luong chuan dau ra lien quan den cac cau hoi xuat hien trong tap mau Giua ky
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1921 trong tap mau Giua ky la %s", length(names(table(GKCDR.1921))))
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1922 trong tap mau Giua ky la %s", length(names(table(GKCDR.1922))))
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1923 trong tap mau Giua ky la %s", length(names(table(GKCDR.1923))))
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1924 trong tap mau Giua ky la %s", length(names(table(GKCDR.1924))))
```

```{r}
# So luong chuan dau ra lien quan den cac cau hoi xuat hien trong tap mau Cuoi ky
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1921 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1921))))
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1922 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1922))))
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1923 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1923))))
sprintf("So luong chuan dau ra lien quan den cac cau hoi xuat hien trong de 1924 trong tap mau Cuoi ky la %s", length(names(table(CKCDR.1924))))
```

```{r}
# Cau IV.2
# Danh sach cac chuan dau ra chi tiet lien quan den cac cau hoi xuat hien trong tap mau Giua ky
# NOTE: names([table-object]) -> lay tat ca 
# NOTE: paste([list-object], collapse = ", ") => chuyen list thanh string cach nhau bang dau ", "
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1921 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1921)), collapse = ', '))
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1922 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1922)), collapse = ', '))
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1923 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1923)), collapse = ', '))
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1924 trong tap mau Giua ky lan luot la %s", paste(names(table(GKCDR.1924)), collapse = ','))
```

```{r}
# Danh sach cac chuan dau ra chi tiet lien quan den cac cau hoi xuat hien trong tap mau Cuoi ky
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1921 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1921)), collapse = ', '))
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1922 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1922)), collapse = ', '))
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1923 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1923)), collapse = ', '))
sprintf("Danh sach cac chuan dau ra lien quan den cac cau hoi xuat hien trong ma de 1924 trong tap mau Cuoi ky lan luot la %s", paste(names(table(CKCDR.1924)), collapse = ', '))
```

```{r fig.height = 15, fig.width = 15}
# Cau IV.3
#Graph Tan suat cua Chuan dau ra o tap mau Giua ky
G3GK.data1921 <- as.data.frame(table(GKCDR.1921))
G3GK.data1922 <- as.data.frame(table(GKCDR.1922))
G3GK.data1923 <- as.data.frame(table(GKCDR.1923))
G3GK.data1924 <- as.data.frame(table(GKCDR.1924))
#MD 1921, 1923, 1924 share mutual frequency

G3GK1921 <- ggplot(G3GK.data1921, aes(x = GKCDR.1921,y =G3GK.data1921[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1921[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1921 ", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "#ffaa80"))

G3GK1922 <- ggplot(G3GK.data1922, aes(x = GKCDR.1922,y =G3GK.data1922[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1922[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "#ffaa80"))

G3GK1923 <- ggplot(G3GK.data1923, aes(x = GKCDR.1923,y =G3GK.data1923[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1923[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "#ffaa80"))

G3GK1924 <- ggplot(G3GK.data1924, aes(x = GKCDR.1924,y =G3GK.data1924[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3GK.data1924[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "#ffaa80"))

G3GK <- ggarrange(G3GK1921, G3GK1922, G3GK1923, G3GK1924, ncol = 2, nrow = 2)
G3GK
# ggsave("hinh/5431_plotchuandauragk.jpg", G3GK)
```

```{r fig.height = 15, fig.width = 15}
#Graph Tan suat cua Chuan dau ra o tap mau Cuoi ky
G3CK.data1921 <- as.data.frame(table(CKCDR.1921))
G3CK.data1922 <- as.data.frame(table(CKCDR.1922))
G3CK.data1923 <- as.data.frame(table(CKCDR.1923))
G3CK.data1924 <- as.data.frame(table(CKCDR.1924))
#MD 1921, 1923, 1924 share mutual frequency

G3CK1921 <- ggplot(G3CK.data1921, aes(x = CKCDR.1921,y =G3CK.data1921[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1921[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1921 ", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "cornsilk"))

G3CK1922 <- ggplot(G3CK.data1922, aes(x = CKCDR.1922,y =G3CK.data1922[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1922[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "cornsilk"))

G3CK1923 <- ggplot(G3CK.data1923, aes(x = CKCDR.1923,y =G3CK.data1923[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1923[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "cornsilk"))

G3CK1924 <- ggplot(G3CK.data1924, aes(x = CKCDR.1924,y =G3CK.data1924[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=G3CK.data1924[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Bieu do tan suat Learning outcome GK-1922", x = "Learning outcome", y = "Frequency") +
  theme(panel.background = element_rect(fill = "cornsilk"))

G3CK <- ggarrange(G3CK1921, G3CK1922, G3CK1923, G3CK1924, ncol = 2, nrow = 2)
G3CK
# ggsave("hinh/5432_plotchuandaurack.jpg", G3GK)
```
```{r fig.height = 20, fig.width = 25}
# Tao dataframe tu file Excel 
DiemGK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 2, startRow = 5)

# Don cac truong co ky tu rong
DiemGK <- DiemGK %>% filter(No != "")
# Tao matrix moi 
DiemGK <- subset(DiemGK, select =  No:MADE)

DiemGK.1921 <- DiemGK %>% filter(MADE == 1921) %>% select(X1:X25) %>% apply(2,as.numeric)
DiemGK.1922 <- DiemGK %>% filter(MADE == 1922) %>% select(X1:X25) %>% apply(2,as.numeric)
DiemGK.1923 <- DiemGK %>% filter(MADE == 1923) %>% select(X1:X25) %>% apply(2,as.numeric)
DiemGK.1924 <- DiemGK %>% filter(MADE == 1924) %>% select(X1:X25) %>% apply(2,as.numeric)

#Add Learning outcome at top of data frame
DiemGK.1921.LO <- as.data.frame(rbind(GKCDR.1921,DiemGK.1921))
DiemGK.1922.LO <- as.data.frame(rbind(GKCDR.1922,DiemGK.1922))
DiemGK.1923.LO <- as.data.frame(rbind(GKCDR.1923,DiemGK.1923))
DiemGK.1924.LO <- as.data.frame(rbind(GKCDR.1924,DiemGK.1924))

#Learning outcome name in midterm
names(table(GKCDR.1921))

#1921
# Phan loai theo cac chuan dau ra
DiemGK.1921.LO11 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 11]
DiemGK.1921.LO12 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 12]
DiemGK.1921.LO21 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 21]
DiemGK.1921.LO22 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 22]
DiemGK.1921.LO31 <- DiemGK.1921.LO[-1,DiemGK.1921.LO[1,] == 31]

# Cong tong theo dong cua dataframe va them Tong moi vao trong data
DiemGK.1921.LO11 <-table(apply(DiemGK.1921.LO11, 1, sum))
DiemGK.1921.LO12 <-table(apply(DiemGK.1921.LO12, 1, sum))
DiemGK.1921.LO21 <-table(apply(DiemGK.1921.LO21, 1, sum))
DiemGK.1921.LO22 <-table(apply(DiemGK.1921.LO22, 1, sum))
DiemGK.1921.LO31 <-table(apply(DiemGK.1921.LO31, 1, sum))


#1922
#Sorting answer into learning outcome group

DiemGK.1922.LO11 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 11]
DiemGK.1922.LO12 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 12]
DiemGK.1922.LO21 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 21]
DiemGK.1922.LO22 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 22]
DiemGK.1922.LO31 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 31]
DiemGK.1922.LO23 <- DiemGK.1922.LO[-1,DiemGK.1922.LO[1,] == 23]

#Sum row data frame and map sum result into instance group
DiemGK.1922.LO11 <-table(apply(DiemGK.1922.LO11, 1, sum))
DiemGK.1922.LO12 <-table(apply(DiemGK.1922.LO12, 1, sum))
DiemGK.1922.LO21 <-table(apply(DiemGK.1922.LO21, 1, sum))
DiemGK.1922.LO22 <-table(apply(DiemGK.1922.LO22, 1, sum))
DiemGK.1922.LO31 <-table(apply(DiemGK.1922.LO31, 1, sum))
DiemGK.1922.LO23 <-table(DiemGK.1922.LO23)
#1923
#Sorting answer into learning outcome group

DiemGK.1923.LO11 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 11]
DiemGK.1923.LO12 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 12]
DiemGK.1923.LO21 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 21]
DiemGK.1923.LO22 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 22]
DiemGK.1923.LO31 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 31]
DiemGK.1923.LO31 <- DiemGK.1923.LO[-1,DiemGK.1923.LO[1,] == 31]

#Sum row data frame and map sum result into instance group
DiemGK.1923.LO11 <-table(apply(DiemGK.1923.LO11, 1, sum))
DiemGK.1923.LO12 <-table(apply(DiemGK.1923.LO12, 1, sum))
DiemGK.1923.LO21 <-table(apply(DiemGK.1923.LO21, 1, sum))
DiemGK.1923.LO22 <-table(apply(DiemGK.1923.LO22, 1, sum))
DiemGK.1923.LO31 <-table(apply(DiemGK.1923.LO31, 1, sum))


#1924
# Phan loai theo cac chuan dau ra
DiemGK.1924.LO11 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 11]
DiemGK.1924.LO12 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 12]
DiemGK.1924.LO21 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 21]
DiemGK.1924.LO22 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 22]
DiemGK.1924.LO31 <- DiemGK.1924.LO[-1,DiemGK.1924.LO[1,] == 31]

# Cong tong theo dong cua dataframe va them Tong moi vao trong data
DiemGK.1924.LO11 <-table(apply(DiemGK.1924.LO11, 1, sum))
DiemGK.1924.LO12 <-table(apply(DiemGK.1924.LO12, 1, sum))
DiemGK.1924.LO21 <-table(apply(DiemGK.1924.LO21, 1, sum))
DiemGK.1924.LO22 <-table(apply(DiemGK.1924.LO22, 1, sum))
DiemGK.1924.LO31 <-table(apply(DiemGK.1924.LO31, 1, sum))

# Learning outcome 11
LOR.GK11 <- as.data.frame(bind_rows(DiemGK.1921.LO11,DiemGK.1922.LO11,DiemGK.1923.LO11,DiemGK.1924.LO11))
# Thay cac gia tri N/A thanh 0
LOR.GK11[is.na(LOR.GK11)] <- 0
# Cong theo dong
LOR.GK11 <- as.data.frame(apply(LOR.GK11,2,sum))
LOR.GK11$SoCauTLDung <- row.names(LOR.GK11)

# xap sep lai data
LOR.GK11<- transform(LOR.GK11, SoCauTLDung = as.numeric(SoCauTLDung))
LOR.GK11<- arrange(LOR.GK11,SoCauTLDung)

#Learning outcome 12 (merge into usable data frame)
LOR.GK12 <- as.data.frame(bind_rows(DiemGK.1921.LO12,DiemGK.1922.LO12,DiemGK.1923.LO12,DiemGK.1924.LO12))
#Fill N/A cell with 0
LOR.GK12[is.na(LOR.GK12)] <- 0
#Sum column
LOR.GK12 <- as.data.frame(apply(LOR.GK12,2,sum))
LOR.GK12$SoCauTLDung <- row.names(LOR.GK12)


#Learning outcome 21 (merge into usable data frame)
LOR.GK21 <- as.data.frame(bind_rows(DiemGK.1921.LO21,DiemGK.1922.LO21,DiemGK.1923.LO21,DiemGK.1924.LO21))
#Fill N/A cell with 0
LOR.GK21[is.na(LOR.GK21)] <- 0
#Sum column
LOR.GK21 <- as.data.frame(apply(LOR.GK21,2,sum))

LOR.GK21$SoCauTLDung <- row.names(LOR.GK21)


#Learning outcome 22 (merge into usable data frame)
LOR.GK22 <- as.data.frame(bind_rows(DiemGK.1921.LO22,DiemGK.1922.LO22,DiemGK.1923.LO22,DiemGK.1924.LO22))
#Fill N/A cell with 0
LOR.GK22[is.na(LOR.GK22)] <- 0
#Sum column
LOR.GK22 <- as.data.frame(apply(LOR.GK22,2,sum))

LOR.GK22$SoCauTLDung <- row.names(LOR.GK22)


#Learning outcome 31 (merge into usable data frame)
LOR.GK31 <- as.data.frame(bind_rows(DiemGK.1921.LO31,DiemGK.1922.LO31,DiemGK.1923.LO31,DiemGK.1924.LO31))
#Fill N/A cell with 0
LOR.GK31[is.na(LOR.GK31)] <- 0
#Sum column
LOR.GK31 <- as.data.frame(apply(LOR.GK31,2,sum))

LOR.GK31$SoCauTLDung <- row.names(LOR.GK31)

#Learning outcome 23
LOR.GK23 <-as.data.frame(DiemGK.1922.LO23)
LOR.GK23$SoCauTraLoiDung <- LOR.GK23[,1]

GLOR.GK11 <- ggplot(LOR.GK11, aes(x = as.character(LOR.GK11[,2]), y = LOR.GK11[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK11[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 11", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOR.GK12 <-  ggplot(LOR.GK12, aes(x = as.character(LOR.GK12[,2]), y = LOR.GK12[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK12[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 12", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOR.GK21 <- ggplot(LOR.GK21, aes(x = as.character(LOR.GK21[,2]), y = LOR.GK21[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK21[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 21", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOR.GK22 <-  ggplot(LOR.GK22, aes(x = as.character(LOR.GK22[,2]), y = LOR.GK22[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK22[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 22", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOR.GK31 <- ggplot(LOR.GK31, aes(x = as.character(LOR.GK31[,2]), y = LOR.GK31[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK31[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 31", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOR.GK23 <- ggplot(LOR.GK23, aes(x = as.character(LOR.GK23[,3]), y = LOR.GK23[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.GK23[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 23", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#d557d5"))


GLOR.GK <-ggarrange(GLOR.GK11,GLOR.GK12,GLOR.GK21,GLOR.GK22,GLOR.GK23,GLOR.GK31, ncol = 3, nrow = 2)
GLOR.GK
# ggsave("hinh/5451_bieudooutcome_gk.jpg", GLOR.GK)
```

```{r fig.height = 15, fig.width = 15}
# Tao dataframe tu file excel cho tap mau Cuoi ky
DiemCK <- read.xlsx2("192_CO1007.xlsx", sheetIndex = 4, startRow = 5)
# Loc No rong
DiemCK <- DiemCK %>% filter(No != "")
# Tao matrix
DiemCK <- subset(DiemCK, select =  No:MADE)

DiemCK.1921 <- DiemCK %>% filter(MADE == 1921) %>% select(X1:X29) %>% apply(2,as.numeric)
DiemCK.1922 <- DiemCK %>% filter(MADE == 1922) %>% select(X1:X29) %>% apply(2,as.numeric)
DiemCK.1923 <- DiemCK %>% filter(MADE == 1923) %>% select(X1:X29) %>% apply(2,as.numeric)
DiemCK.1924 <- DiemCK %>% filter(MADE == 1924) %>% select(X1:X29) %>% apply(2,as.numeric)

#Add Learning outcome at top of data frame
DiemCK.1921.LO <- as.data.frame(rbind(CKCDR.1921,DiemCK.1921))
DiemCK.1922.LO <- as.data.frame(rbind(CKCDR.1922,DiemCK.1922))
DiemCK.1923.LO <- as.data.frame(rbind(CKCDR.1923,DiemCK.1923))
DiemCK.1924.LO <- as.data.frame(rbind(CKCDR.1924,DiemCK.1924))

#Learning outcome name in final exam
names(table(CKCDR.1921))

#1921
#S Phan loai theo cac chuan dau ra
DiemCK.1921.LO12 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 12]
DiemCK.1921.LO23 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 23]
DiemCK.1921.LO31 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 31]
DiemCK.1921.LO32 <- DiemCK.1921.LO[-1,DiemCK.1921.LO[1,] == 32]

# Cong tong theo dong cua dataframe va them Tong moi vao trong data
DiemCK.1921.LO12 <-table(apply(DiemCK.1921.LO12, 1, sum))
DiemCK.1921.LO23 <-table(apply(DiemCK.1921.LO23, 1, sum))
DiemCK.1921.LO31 <-table(apply(DiemCK.1921.LO31, 1, sum))
DiemCK.1921.LO32 <-table(apply(DiemCK.1921.LO32, 1, sum))

#1922
#Sorting answer into learning outcome group


DiemCK.1922.LO12 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 12]
DiemCK.1922.LO23 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 23]
DiemCK.1922.LO31 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 31]
DiemCK.1922.LO32 <- DiemCK.1922.LO[-1,DiemCK.1922.LO[1,] == 32]

#Sum row data frame and map sum result into instance group

DiemCK.1922.LO12 <-table(apply(DiemCK.1922.LO12, 1, sum))
DiemCK.1922.LO23 <-table(apply(DiemCK.1922.LO23, 1, sum))
DiemCK.1922.LO31 <-table(apply(DiemCK.1922.LO31, 1, sum))
DiemCK.1922.LO32 <-table(apply(DiemCK.1922.LO32, 1, sum))

#1923
#Sorting answer into learning outcome group


DiemCK.1923.LO12 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 12]
DiemCK.1923.LO23 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 23]
DiemCK.1923.LO31 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 31]
DiemCK.1923.LO32 <- DiemCK.1923.LO[-1,DiemCK.1923.LO[1,] == 32]

#Sum row data frame and map sum result into instance group

DiemCK.1923.LO12 <-table(apply(DiemCK.1923.LO12, 1, sum))
DiemCK.1923.LO23 <-table(apply(DiemCK.1923.LO23, 1, sum))
DiemCK.1923.LO31 <-table(apply(DiemCK.1923.LO31, 1, sum))
DiemCK.1923.LO32 <-table(apply(DiemCK.1923.LO32, 1, sum))

#1924
#Sorting answer into learning outcome group


DiemCK.1924.LO12 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 12]
DiemCK.1924.LO23 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 23]
DiemCK.1924.LO31 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 31]
DiemCK.1924.LO32 <- DiemCK.1924.LO[-1,DiemCK.1924.LO[1,] == 32]

#Sum row data frame and map sum result into instance group

DiemCK.1924.LO12 <-table(apply(DiemCK.1924.LO12, 1, sum))
DiemCK.1924.LO23 <-table(apply(DiemCK.1924.LO23, 1, sum))
DiemCK.1924.LO31 <-table(apply(DiemCK.1924.LO31, 1, sum))
DiemCK.1924.LO32 <-table(apply(DiemCK.1924.LO32, 1, sum))

#Learning outcome 12
LOR.CK12 <- as.data.frame(bind_rows(DiemCK.1921.LO12,DiemCK.1922.LO12,DiemCK.1923.LO12,DiemCK.1924.LO12))

# Thay cac gia tri N/A thanh 0
LOR.CK12[is.na(LOR.CK12)] <- 0
# Cong cot
LOR.CK12 <- as.data.frame(apply(LOR.CK12,2,sum))

# Sap xep lai data
LOR.CK12$SoCauTLDung <- row.names(LOR.CK12)

#Learning outcome 23 (merge into usable data frame)

LOR.CK23 <- as.data.frame(bind_rows(DiemCK.1921.LO23,DiemCK.1922.LO23,DiemCK.1923.LO23,DiemCK.1924.LO23))

#Fill N/A cell with 0
LOR.CK23[is.na(LOR.CK23)] <- 0
#Sum column
LOR.CK23 <- as.data.frame(apply(LOR.CK23,2,sum))

LOR.CK23$SoCauTLDung <- row.names(LOR.CK23)

#Learning outcome 31 (merge into usable data frame)

LOR.CK31 <- as.data.frame(bind_rows(DiemCK.1921.LO31,DiemCK.1922.LO31,DiemCK.1923.LO31,DiemCK.1924.LO31))

#Fill N/A cell with 0
LOR.CK31[is.na(LOR.CK31)] <- 0
#Sum column
LOR.CK31 <- as.data.frame(apply(LOR.CK31,2,sum))

LOR.CK31$SoCauTLDung <- row.names(LOR.CK31)
LOR.CK31

#Learning outcome 32 (merge into usable data frame)
LOR.CK32 <- as.data.frame(bind_rows(DiemCK.1921.LO32,DiemCK.1922.LO32,DiemCK.1923.LO32,DiemCK.1924.LO32))

#Fill N/A cell with 0
LOR.CK32[is.na(LOR.CK32)] <- 0
#Sum column
LOR.CK32 <- as.data.frame(apply(LOR.CK32,2,sum))

LOR.CK32$SoCauTLDung <- row.names(LOR.CK32)

#Graph ----
GLOR.CK12 <-ggplot(LOR.CK12, aes(x = as.character(LOR.CK12[,2]), y = LOR.CK12[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK12[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 12", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))

GLOR.CK23 <- ggplot(LOR.CK23, aes(x = as.character(LOR.CK23[,2]), y = LOR.CK23[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK23[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 23", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))

GLOR.CK31 <-ggplot(LOR.CK31, aes(x = as.character(LOR.CK31[,2]), y = LOR.CK31[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK31[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 31", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))

GLOR.CK32 <- ggplot(LOR.CK32, aes(x = as.character(LOR.CK32[,2]), y = LOR.CK32[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LOR.CK32[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 32", x = "So cau tra loi dung", y = "So sinh vien") + theme(panel.background = element_rect(fill = "#57d5c5"))

GLOR.CK <-ggarrange(GLOR.CK12,GLOR.CK23,GLOR.CK31,GLOR.CK32, ncol = 2, nrow = 2)
GLOR.CK
ggsave("hinh/5452_bieudooutcome_ck.jpg", GLOR.CK)
```
```{r}
# Cau IV.7
# Data cho Chuan dau ra Giua ky va Cuoi ky
LO.data<- as.data.frame(bind_rows(table(GKCDR.1921),table(CKCDR.1921)))
# Chuuyen doi thanh data dung de ve bieu do
LO.data[is.na(LO.data)] <- 0
LO.data<- as.data.frame(apply(LO.data,2,sum))
LO.data$LO <- row.names(LO.data)
LO.data

# Do thi 
GLO.data <- ggplot(LO.data, aes(x = as.character(LO.data[,2]), y = LO.data[,1])) + geom_bar(stat = "identity") + geom_text(aes(label=LO.data[,1]), vjust=-0.3, size=3.5, )+
  labs(title = "Question per learning outcome", x = "Learning outcome", y = "Questions quantity") + theme(panel.background = element_rect(fill = "#57d5c5"))
GLO.data

ggsave("hinh/5471_bieudocauhoi.jpg", GLO.data)

#
TotalQFrameCK<-as.data.frame(table(as.numeric(CKCDR[1,-1])))

TotalQFrameGK<-as.data.frame(table(as.numeric(GKCDR[1,-1])))

TotalQFrameGKEX<-as.data.frame(bind_rows(table(GKCDR.1921),
                                         table(GKCDR.1922),
                                         table(GKCDR.1923),
                                         table(GKCDR.1924)))
TotalQFrameGKEX[is.na(TotalQFrameGKEX)] <- 0
#exception for 1922 LO 23 & 12
TotalQFrameGKEX
```

```{r fig.height = 50, fig.width = 50}
# Cau IV.8
#convert data from problem 6 for reuse

LOW.GK11 <- LOR.GK11
LOW.GK11 <- transform(LOW.GK11, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK11$SoCauTLSai <- abs(LOW.GK11$SoCauTLDung - as.numeric(TotalQFrameGK[1,2]))
LOW.GK11 <- LOW.GK11[,c(1,3)]

#percentaging
LOW.GK11$percent <- round(LOW.GK11[,1]/sum(LOW.GK11[,1]),3)
LOW.GK11

#exception handling for Learning outcome 12 


LOW.GK12.1921.3.4<- as.data.frame(bind_rows(DiemGK.1921.LO12,
                                            DiemGK.1923.LO12,
                                            DiemGK.1924.LO12,
                                            ))
#fill na with 0
LOW.GK12.1921.3.4[is.na(LOW.GK12.1921.3.4)] <- 0

LOW.GK12.1921.3.4<- as.data.frame(apply(LOW.GK12.1921.3.4,2,sum))
LOW.GK12.1921.3.4$SoCauTLSai <- abs(as.numeric(row.names(LOW.GK12.1921.3.4)) - 12)

#percentaging
LOW.GK12.1921.3.4$percent <-round(LOW.GK12.1921.3.4[,1]/sum(LOW.GK12.1921.3.4[,1]),3)

LOW.GK12.1922<-t(as.data.frame(bind_rows(DiemGK.1922.LO12)))

temp1922 <- abs(as.numeric(row.names(LOW.GK12.1922)) - 11)

LOW.GK12.1922<-as.data.frame(DiemGK.1922.LO12)
LOW.GK12.1922$SoCauTLSai <- as.numeric(temp1922)

LOW.GK12.1922$percent <- round(LOW.GK12.1922[,2]/sum(LOW.GK12.1922[,2]),3)
LOW.GK12.1922<- LOW.GK12.1922[,c(3,4)]

#reserve

DiemGK.1921.LO12.mod <- DiemGK.1921.LO12
DiemGK.1922.LO12.mod <- DiemGK.1922.LO12
DiemGK.1923.LO12.mod <- DiemGK.1923.LO12
DiemGK.1924.LO12.mod <- DiemGK.1924.LO12

rownames(DiemGK.1921.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1921.LO12)) -12)
rownames(DiemGK.1922.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1922.LO12)) -11)
rownames(DiemGK.1923.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1923.LO12)) -12)
rownames(DiemGK.1924.LO12.mod)<- abs(as.numeric(rownames(DiemGK.1924.LO12)) -12)

LOW.GK12 <- as.data.frame(bind_rows(DiemGK.1921.LO12.mod,
                                    DiemGK.1922.LO12.mod,
                                    DiemGK.1923.LO12.mod,
                                    DiemGK.1924.LO12.mod,
))

LOW.GK12[is.na(LOW.GK12)] <- 0
as.numeric(row.names(DiemGK.1921.LO12)) -1
LOW.GK12<-as.data.frame(apply(LOW.GK12,2,sum))
LOW.GK12
LOW.GK12$SoCauTLSai <- abs(as.numeric(row.names(LOW.GK12)))
LOW.GK12$percent <- round(LOW.GK12[,1]/sum(LOW.GK12[,1]),3)
LOW.GK12


LOW.GK21 <- LOR.GK21
LOW.GK21 <- transform(LOW.GK21, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK21$SoCauTLSai <- abs(LOW.GK21$SoCauTLDung - as.numeric(TotalQFrameGK[3,2]))
LOW.GK21 <- LOW.GK21[,c(1,3)]
LOW.GK21
#percentaging
LOW.GK21$percent <- round(LOW.GK21[,1]/sum(LOW.GK21[,1]),3)
LOW.GK21


LOW.GK22 <- LOR.GK22
LOW.GK22 <- transform(LOW.GK22, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK22$SoCauTLSai <- abs(LOW.GK22$SoCauTLDung - as.numeric(TotalQFrameGK[4,2]))
LOW.GK22 <- LOW.GK22[,c(1,3)]

#percentaging
LOW.GK22$percent <- round(LOW.GK22[,1]/sum(LOW.GK22[,1]),3)
LOW.GK22

LOW.GK31 <- LOR.GK31
LOW.GK31 <- transform(LOW.GK31, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.GK31$SoCauTLSai <- abs(LOW.GK31$SoCauTLDung - as.numeric(TotalQFrameGK[5,2]))
LOW.GK31 <- LOW.GK31[,c(1,3)]

#percentaging
LOW.GK31$percent <- round(LOW.GK31[,1]/sum(LOW.GK31[,1]),3)
LOW.GK31

#exception for 1922 code
LOW.GK23 <- LOR.GK23
LOW.GK23 <- transform(LOW.GK23, SoCauTraLoiDung = as.numeric(SoCauTraLoiDung))
LOW.GK23$SoCauTLSai <- abs(as.numeric(LOW.GK23$SoCauTraLoiDung) - 2)
LOW.GK23 <- LOW.GK23[,c(2,4)]

#percentaging
LOW.GK23$percent <- round(LOW.GK23[,1]/sum(LOW.GK23[,1]),3)
LOW.GK23

#Graph ---

GLOW.GK11 <- ggplot(LOW.GK11, aes(x = as.character(LOW.GK11[,2]), y = LOW.GK11[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK11[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 11", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.GK12 <-  ggplot(LOW.GK12, aes(x = as.character(LOW.GK12[,2]), y = LOW.GK12[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK12[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 12", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.GK21 <- ggplot(LOW.GK21, aes(x = as.character(LOW.GK21[,2]), y = LOW.GK21[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK21[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 21", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.GK22 <-  ggplot(LOW.GK22, aes(x = as.character(LOW.GK22[,2]), y = LOW.GK22[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK22[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 22", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.GK31 <- ggplot(LOW.GK31, aes(x = as.character(LOW.GK31[,2]), y = LOW.GK31[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK31[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.GK23 <- ggplot(LOW.GK23, aes(x = as.character(LOW.GK23[,2]), y = LOW.GK23[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK23[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.GK12.1.3.4 <- ggplot(LOW.GK12.1921.3.4, aes(x = as.character(LOW.GK12.1921.3.4[,2]), y = LOW.GK12.1921.3.4[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.GK12.1921.3.4[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 12-1921-3-4", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.GK12.2 <- ggplot(LOW.GK12.1922, aes(x = as.character(LOW.GK12.1922[,1]), y = LOW.GK12.1922[,2])) + geom_bar(stat = 
                                                                                                                   "identity") + geom_text(aes(label=LOW.GK12.1922[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 12-1922", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))


GLOW.GK <-ggarrange(GLOW.GK11,GLOW.GK12.2,GLOW.GK12.1.3.4,GLOW.GK12,GLOW.GK21,GLOW.GK22,GLOW.GK23,GLOW.GK31, ncol = 3, nrow = 3)
GLOW.GK
# ggsave("hinh/5481_bieudotansuatcausaigk.jpg", GLOW.GK)
```

```{r}
# Cau IV.9
#convert data from problem 6 for reuse

LOW.CK12 <- LOR.CK12
LOW.CK12 <- transform(LOW.CK12, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK12$SoCauTLSai <- abs(LOW.CK12$SoCauTLDung - as.numeric(TotalQFrameCK[1,2]))
LOW.CK12 <- LOW.CK12[,c(1,3)]

#percentaging
LOW.CK12$percent <- round(LOW.CK12[,1]/sum(LOW.CK12[,1]),3)
LOW.CK12

LOW.CK23 <- LOR.CK23
LOW.CK23 <- transform(LOW.CK23, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK23$SoCauTLSai <- abs(LOW.CK23$SoCauTLDung - as.numeric(TotalQFrameCK[2,2]))
LOW.CK23 <- LOW.CK23[,c(1,3)]

#percentaging
LOW.CK23$percent <- round(LOW.CK23[,1]/sum(LOW.CK23[,1]),3)
LOW.CK23

LOW.CK31 <- LOR.CK31
LOW.CK31 <- transform(LOW.CK31, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK31$SoCauTLSai <- abs(LOW.CK31$SoCauTLDung - as.numeric(TotalQFrameCK[3,2]))
LOW.CK31 <- LOW.CK31[,c(1,3)]

#percentaging
LOW.CK31$percent <- round(LOW.CK31[,1]/sum(LOW.CK31[,1]),3)
LOW.CK31

LOW.CK32 <- LOR.CK32
LOW.CK32 <- transform(LOW.CK32, SoCauTLDung = as.numeric(SoCauTLDung))
LOW.CK32$SoCauTLSai <- abs(LOW.CK32$SoCauTLDung - as.numeric(TotalQFrameCK[4,2]))
LOW.CK32 <- LOW.CK32[,c(1,3)]

#percentaging
LOW.CK32$percent <- round(LOW.CK32[,1]/sum(LOW.CK32[,1]),3)
LOW.CK32

#Graph---

GLOW.CK12 <- ggplot(LOW.CK12, aes(x = as.character(LOW.CK12[,2]), y = LOW.CK12[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK12[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 12", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.CK23 <- ggplot(LOW.CK23, aes(x = as.character(LOW.CK23[,2]), y = LOW.CK23[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK23[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 23", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.CK31 <- ggplot(LOW.CK31, aes(x = as.character(LOW.CK31[,2]), y = LOW.CK31[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK31[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.CK32 <- ggplot(LOW.CK32, aes(x = as.character(LOW.CK32[,2]), y = LOW.CK32[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW.CK32[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 32", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

GLOW.CK <-ggarrange(GLOW.CK12,GLOW.CK23,GLOW.CK31,GLOW.CK32, ncol = 3, nrow = 2)

GLOW.CK
```

```{r fig.height = 50, fig.width = 75}
# Cau IV.10
LoListCK <- data.frame(LO= names(table(CKCDR.1922)))
LoListGK <- data.frame(LO= names(table(GKCDR.1922)))
LoAp<-as.data.frame(table(rbind(LoListCK,LoListGK)))

#Lo appeared in both midterm and final
LoAp[LoAp$Freq == 2,]
TotalQFrameCK[TotalQFrameCK$Var1 == 12,]
TotalQFrameGK[TotalQFrameGK$Var1 == 12,]

#Number of students in mid/final 
nrow(DiemGK)
nrow(DiemCK)
#create data with 361 students(based on final)
#we will deal with 5 last students later

DiemGKCK<- cbind( DiemGK[1:361,],DiemCK)
#Ma de pool
md <- as.numeric(CKCDR[,1])
#add combined made
DiemGKCK$mdCK_GK <- as.numeric(paste(DiemGKCK[,29] ,DiemGKCK[,62], sep = ""))
#rearrange
DiemGKCK <- DiemGKCK[,c(63,1,4:28,33:61)]
colnames(DiemGKCK)[c(3:56)] <- c(1:54)

#rename column
GKCDR.colrename <-GKCDR
CKCDR.colrename <-CKCDR
colnames(GKCDR.colrename)[2:26] <- c(1:25)
colnames(CKCDR.colrename)[2:30] <- c(26:54)


MD1921.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1921.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1921.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1921.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1921,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])
MD1922.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1922.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1922.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1922.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1922,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])
MD1923.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1923.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1923.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1923.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1923,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])
MD1924.1921 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1921,-1])
MD1924.1922 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1922,-1])
MD1924.1923 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1923,-1])
MD1924.1924 <- cbind(GKCDR.colrename[GKCDR.colrename[1] == 1924,-1],
                     CKCDR.colrename[CKCDR.colrename[1] == 1924,-1])

ncol(MD1924.1924)

Diem1921.1921<-DiemGKCK[DiemGKCK[1] == 19211921,]
Diem1921.1922<-DiemGKCK[DiemGKCK[1] == 19211922,]
Diem1921.1923<-DiemGKCK[DiemGKCK[1] == 19211923,]
Diem1921.1924<-DiemGKCK[DiemGKCK[1] == 19211924,]
Diem1922.1921<-DiemGKCK[DiemGKCK[1] == 19221921,]
Diem1922.1922<-DiemGKCK[DiemGKCK[1] == 19221922,]
Diem1922.1923<-DiemGKCK[DiemGKCK[1] == 19221923,]
Diem1922.1924<-DiemGKCK[DiemGKCK[1] == 19221924,]
Diem1923.1921<-DiemGKCK[DiemGKCK[1] == 19231921,]
Diem1923.1922<-DiemGKCK[DiemGKCK[1] == 19231922,]
Diem1923.1923<-DiemGKCK[DiemGKCK[1] == 19231923,]
Diem1923.1924<-DiemGKCK[DiemGKCK[1] == 19231924,]
Diem1924.1921<-DiemGKCK[DiemGKCK[1] == 19241921,]
Diem1924.1922<-DiemGKCK[DiemGKCK[1] == 19241922,]
Diem1924.1923<-DiemGKCK[DiemGKCK[1] == 19241923,]
Diem1924.1924<-DiemGKCK[DiemGKCK[1] == 19241924,]

Diem1921.1921 <- apply(bind_rows(MD1921.1921,Diem1921.1921[,c(3:56)]),2,as.numeric)
Diem1921.1922 <- apply(bind_rows(MD1921.1922,Diem1921.1922[,c(3:56)]),2,as.numeric)
Diem1921.1923 <- apply(bind_rows(MD1921.1923,Diem1921.1923[,c(3:56)]),2,as.numeric)
Diem1921.1924 <- apply(bind_rows(MD1921.1924,Diem1921.1924[,c(3:56)]),2,as.numeric)
Diem1922.1921 <- apply(bind_rows(MD1922.1921,Diem1922.1921[,c(3:56)]),2,as.numeric)
Diem1922.1922 <- apply(bind_rows(MD1922.1922,Diem1922.1922[,c(3:56)]),2,as.numeric)
Diem1922.1923 <- apply(bind_rows(MD1922.1923,Diem1922.1923[,c(3:56)]),2,as.numeric)
Diem1922.1924 <- apply(bind_rows(MD1922.1924,Diem1922.1924[,c(3:56)]),2,as.numeric)
Diem1923.1921 <- apply(bind_rows(MD1923.1921,Diem1923.1921[,c(3:56)]),2,as.numeric)
Diem1923.1922 <- apply(bind_rows(MD1923.1922,Diem1923.1922[,c(3:56)]),2,as.numeric)
Diem1923.1923 <- apply(bind_rows(MD1923.1923,Diem1923.1923[,c(3:56)]),2,as.numeric)
Diem1923.1924 <- apply(bind_rows(MD1923.1924,Diem1923.1924[,c(3:56)]),2,as.numeric)
Diem1924.1921 <- apply(bind_rows(MD1924.1921,Diem1924.1921[,c(3:56)]),2,as.numeric)
Diem1924.1922 <- apply(bind_rows(MD1924.1922,Diem1924.1922[,c(3:56)]),2,as.numeric)
Diem1924.1923 <- apply(bind_rows(MD1924.1923,Diem1924.1923[,c(3:56)]),2,as.numeric)
Diem1924.1924 <- apply(bind_rows(MD1924.1924,Diem1924.1924[,c(3:56)]),2,as.numeric)

#last 5 students did midterm exam but did not do final exam 
last5.1922 <- DiemGK[362:366,]
last5.1923 <- DiemGK[362:366,]
last5.1924 <- DiemGK[362:366,]
last5.1922 <- last5.1922 %>% filter(MADE==1922) %>% select(X1:X25) %>% apply(2,as.numeric)
last5.1923 <- last5.1923 %>% filter(MADE==1923) %>% select(X1:X25) %>% apply(2,as.numeric)
last5.1924 <- last5.1924 %>% filter(MADE==1924) %>% select(X1:X25) %>% apply(2,as.numeric)

last5.1922 <- as.data.frame(rbind(GKCDR.1922,last5.1922))
last5.1923 <- as.data.frame(rbind(GKCDR.1923,last5.1923))
last5.1924 <- as.data.frame(rbind(GKCDR.1924,last5.1924))


#Learning outcome 12

LO12.last5.1922 <- table(apply(last5.1922[-1,last5.1922[1,]==12],1,sum))
LO12.last5.1923 <- table(apply(last5.1923[-1,last5.1923[1,]==12],1,sum))
LO12.last5.1924 <- table(apply(last5.1924[-1,last5.1924[1,]==12],1,sum))

rownames(LO12.last5.1922) <- abs(as.numeric(rownames(LO12.last5.1922)) - ncol(GKCDR[GKCDR[,1]==1922,GKCDR[2,] == 12]))
rownames(LO12.last5.1923) <- abs(as.numeric(rownames(LO12.last5.1923)) - ncol(GKCDR[GKCDR[,1]==1923,GKCDR[2,] == 12]))
rownames(LO12.last5.1924) <- abs(as.numeric(rownames(LO12.last5.1924)) - ncol(GKCDR[GKCDR[,1]==1924,GKCDR[2,] == 12]))


LO12.Diem1921.1921 <- table(apply(Diem1921.1921[-1,Diem1921.1921[1,]==12],1,sum))
LO12.Diem1921.1922 <- table(apply(Diem1921.1922[-1,Diem1921.1922[1,]==12],1,sum))
LO12.Diem1921.1923 <- table(apply(Diem1921.1923[-1,Diem1921.1923[1,]==12],1,sum))
LO12.Diem1921.1924 <- table(apply(Diem1921.1924[-1,Diem1921.1924[1,]==12],1,sum))
LO12.Diem1922.1921 <- table(apply(Diem1922.1921[-1,Diem1922.1921[1,]==12],1,sum))
LO12.Diem1922.1922 <- table(apply(Diem1922.1922[-1,Diem1922.1922[1,]==12],1,sum))
LO12.Diem1922.1923 <- table(apply(Diem1922.1923[-1,Diem1922.1923[1,]==12],1,sum))
LO12.Diem1922.1924 <- table(apply(Diem1922.1924[-1,Diem1922.1924[1,]==12],1,sum))
LO12.Diem1923.1921 <- table(apply(Diem1923.1921[-1,Diem1923.1921[1,]==12],1,sum))
LO12.Diem1923.1922 <- table(apply(Diem1923.1922[-1,Diem1923.1922[1,]==12],1,sum))
LO12.Diem1923.1923 <- table(apply(Diem1923.1923[-1,Diem1923.1923[1,]==12],1,sum))
LO12.Diem1923.1924 <- table(apply(Diem1923.1924[-1,Diem1923.1924[1,]==12],1,sum))
LO12.Diem1924.1921 <- table(apply(Diem1924.1921[-1,Diem1924.1921[1,]==12],1,sum))
LO12.Diem1924.1922 <- table(apply(Diem1924.1922[-1,Diem1924.1922[1,]==12],1,sum))
LO12.Diem1924.1923 <- table(apply(Diem1924.1923[-1,Diem1924.1923[1,]==12],1,sum))
LO12.Diem1924.1924 <- table(apply(Diem1924.1924[-1,Diem1924.1924[1,]==12],1,sum))

#make count right answer -> wrong answer
rownames(LO12.Diem1921.1921) <- abs(as.numeric(rownames(LO12.Diem1921.1921)) - ncol(MD1921.1921[,MD1921.1921[1,]==12]))
rownames(LO12.Diem1921.1922) <- abs(as.numeric(rownames(LO12.Diem1921.1922)) - ncol(MD1921.1922[,MD1921.1922[1,]==12]))
rownames(LO12.Diem1921.1923) <- abs(as.numeric(rownames(LO12.Diem1921.1923)) - ncol(MD1921.1923[,MD1921.1923[1,]==12]))
rownames(LO12.Diem1921.1924) <- abs(as.numeric(rownames(LO12.Diem1921.1924)) - ncol(MD1921.1924[,MD1921.1924[1,]==12]))

#different is here
rownames(LO12.Diem1922.1921) <- abs(as.numeric(rownames(LO12.Diem1922.1921)) - ncol(MD1922.1921[,MD1922.1921[1,]==12]))
rownames(LO12.Diem1922.1922) <- abs(as.numeric(rownames(LO12.Diem1922.1922)) - ncol(MD1922.1922[,MD1922.1922[1,]==12]))
rownames(LO12.Diem1922.1923) <- abs(as.numeric(rownames(LO12.Diem1922.1923)) - ncol(MD1922.1923[,MD1922.1923[1,]==12]))
rownames(LO12.Diem1922.1924) <- abs(as.numeric(rownames(LO12.Diem1922.1924)) - ncol(MD1922.1924[,MD1922.1924[1,]==12]))


#-----------------

rownames(LO12.Diem1923.1921) <- abs(as.numeric(rownames(LO12.Diem1923.1921)) - ncol(MD1923.1921[,MD1923.1921[1,]==12]))
rownames(LO12.Diem1923.1922) <- abs(as.numeric(rownames(LO12.Diem1923.1922)) - ncol(MD1923.1922[,MD1923.1922[1,]==12]))
rownames(LO12.Diem1923.1923) <- abs(as.numeric(rownames(LO12.Diem1923.1923)) - ncol(MD1923.1923[,MD1923.1923[1,]==12]))
rownames(LO12.Diem1923.1924) <- abs(as.numeric(rownames(LO12.Diem1923.1924)) - ncol(MD1923.1924[,MD1923.1924[1,]==12]))
rownames(LO12.Diem1924.1921) <- abs(as.numeric(rownames(LO12.Diem1924.1921)) - ncol(MD1924.1921[,MD1924.1921[1,]==12]))
rownames(LO12.Diem1924.1922) <- abs(as.numeric(rownames(LO12.Diem1924.1922)) - ncol(MD1924.1922[,MD1924.1922[1,]==12]))
rownames(LO12.Diem1924.1923) <- abs(as.numeric(rownames(LO12.Diem1924.1923)) - ncol(MD1924.1923[,MD1924.1923[1,]==12]))
rownames(LO12.Diem1924.1924) <- abs(as.numeric(rownames(LO12.Diem1924.1924)) - ncol(MD1924.1924[,MD1924.1924[1,]==12]))




LOW12 <- as.data.frame(bind_rows(LO12.Diem1921.1921,
                                 LO12.Diem1921.1922,
                                 LO12.Diem1921.1923,
                                 LO12.Diem1921.1924,
                                 LO12.Diem1922.1921,
                                 LO12.Diem1922.1922,
                                 LO12.Diem1922.1923,
                                 LO12.Diem1922.1924,
                                 LO12.Diem1923.1921,
                                 LO12.Diem1923.1922,
                                 LO12.Diem1923.1923,
                                 LO12.Diem1923.1924,
                                 LO12.Diem1924.1921,
                                 LO12.Diem1924.1922,
                                 LO12.Diem1924.1923,
                                 LO12.Diem1924.1924,
                                 LO12.last5.1922,
                                 LO12.last5.1923,
                                 LO12.last5.1924
))

LOW12[is.na(LOW12)] <- 0

LOW12 <- as.data.frame(apply(LOW12,2,sum))
LOW12$SoCauTLSai <- as.numeric(row.names(LOW12))

LOW12$percent <- round(LOW12[,1]/sum(LOW12[,1]),3)

#graph----

GLOW12 <- ggplot(LOW12, aes(x = as.character(LOW12[,2]), y = LOW12[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW12[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 12", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))


#Learning outcome 23

#expected to be 1

LO23.last5.1922 <- table(last5.1922[-1,last5.1922[1,]==23])
#mini conclude: 1 question related to LO23 on Made1922 was answer by 1 student
#and his/her answer was right -> LO23.last5.1922 = 0.(no need to add to combined)

LO23.Diem1921.1921 <- table(apply(Diem1921.1921[-1,Diem1921.1921[1,]==23],1,sum))
LO23.Diem1921.1922 <- table(apply(Diem1921.1922[-1,Diem1921.1922[1,]==23],1,sum))
LO23.Diem1921.1923 <- table(apply(Diem1921.1923[-1,Diem1921.1923[1,]==23],1,sum))
LO23.Diem1921.1924 <- table(apply(Diem1921.1924[-1,Diem1921.1924[1,]==23],1,sum))
LO23.Diem1922.1921 <- table(apply(Diem1922.1921[-1,Diem1922.1921[1,]==23],1,sum))
LO23.Diem1922.1922 <- table(apply(Diem1922.1922[-1,Diem1922.1922[1,]==23],1,sum))
LO23.Diem1922.1923 <- table(apply(Diem1922.1923[-1,Diem1922.1923[1,]==23],1,sum))
LO23.Diem1922.1924 <- table(apply(Diem1922.1924[-1,Diem1922.1924[1,]==23],1,sum))
LO23.Diem1923.1921 <- table(apply(Diem1923.1921[-1,Diem1923.1921[1,]==23],1,sum))
LO23.Diem1923.1922 <- table(apply(Diem1923.1922[-1,Diem1923.1922[1,]==23],1,sum))
LO23.Diem1923.1923 <- table(apply(Diem1923.1923[-1,Diem1923.1923[1,]==23],1,sum))
LO23.Diem1923.1924 <- table(apply(Diem1923.1924[-1,Diem1923.1924[1,]==23],1,sum))
LO23.Diem1924.1921 <- table(apply(Diem1924.1921[-1,Diem1924.1921[1,]==23],1,sum))
LO23.Diem1924.1922 <- table(apply(Diem1924.1922[-1,Diem1924.1922[1,]==23],1,sum))
LO23.Diem1924.1923 <- table(apply(Diem1924.1923[-1,Diem1924.1923[1,]==23],1,sum))
LO23.Diem1924.1924 <- table(apply(Diem1924.1924[-1,Diem1924.1924[1,]==23],1,sum))

#make count right answer -> wrong answer
rownames(LO23.Diem1921.1921) <- abs(as.numeric(rownames(LO23.Diem1921.1921)) - ncol(MD1921.1921[,MD1921.1921[1,]==23]))
rownames(LO23.Diem1921.1922) <- abs(as.numeric(rownames(LO23.Diem1921.1922)) - ncol(MD1921.1922[,MD1921.1922[1,]==23]))
rownames(LO23.Diem1921.1923) <- abs(as.numeric(rownames(LO23.Diem1921.1923)) - ncol(MD1921.1923[,MD1921.1923[1,]==23]))
rownames(LO23.Diem1921.1924) <- abs(as.numeric(rownames(LO23.Diem1921.1924)) - ncol(MD1921.1924[,MD1921.1924[1,]==23]))

#different is here
rownames(LO23.Diem1922.1921) <- abs(as.numeric(rownames(LO23.Diem1922.1921)) - ncol(MD1922.1921[,MD1922.1921[1,]==23]))
rownames(LO23.Diem1922.1922) <- abs(as.numeric(rownames(LO23.Diem1922.1922)) - ncol(MD1922.1922[,MD1922.1922[1,]==23]))
rownames(LO23.Diem1922.1923) <- abs(as.numeric(rownames(LO23.Diem1922.1923)) - ncol(MD1922.1923[,MD1922.1923[1,]==23]))
rownames(LO23.Diem1922.1924) <- abs(as.numeric(rownames(LO23.Diem1922.1924)) - ncol(MD1922.1924[,MD1922.1924[1,]==23]))


#-----------------

rownames(LO23.Diem1923.1921) <- abs(as.numeric(rownames(LO23.Diem1923.1921)) - ncol(MD1923.1921[,MD1923.1921[1,]==23]))
rownames(LO23.Diem1923.1922) <- abs(as.numeric(rownames(LO23.Diem1923.1922)) - ncol(MD1923.1922[,MD1923.1922[1,]==23]))
rownames(LO23.Diem1923.1923) <- abs(as.numeric(rownames(LO23.Diem1923.1923)) - ncol(MD1923.1923[,MD1923.1923[1,]==23]))
rownames(LO23.Diem1923.1924) <- abs(as.numeric(rownames(LO23.Diem1923.1924)) - ncol(MD1923.1924[,MD1923.1924[1,]==23]))
rownames(LO23.Diem1924.1921) <- abs(as.numeric(rownames(LO23.Diem1924.1921)) - ncol(MD1924.1921[,MD1924.1921[1,]==23]))
rownames(LO23.Diem1924.1922) <- abs(as.numeric(rownames(LO23.Diem1924.1922)) - ncol(MD1924.1922[,MD1924.1922[1,]==23]))
rownames(LO23.Diem1924.1923) <- abs(as.numeric(rownames(LO23.Diem1924.1923)) - ncol(MD1924.1923[,MD1924.1923[1,]==23]))
rownames(LO23.Diem1924.1924) <- abs(as.numeric(rownames(LO23.Diem1924.1924)) - ncol(MD1924.1924[,MD1924.1924[1,]==23]))



LOW23 <- as.data.frame(bind_rows(LO23.Diem1921.1921,
                                 LO23.Diem1921.1922,
                                 LO23.Diem1921.1923,
                                 LO23.Diem1921.1924,
                                 LO23.Diem1922.1921,
                                 LO23.Diem1922.1922,
                                 LO23.Diem1922.1923,
                                 LO23.Diem1922.1924,
                                 LO23.Diem1923.1921,
                                 LO23.Diem1923.1922,
                                 LO23.Diem1923.1923,
                                 LO23.Diem1923.1924,
                                 LO23.Diem1924.1921,
                                 LO23.Diem1924.1922,
                                 LO23.Diem1924.1923,
                                 LO23.Diem1924.1924,

                                 
))

LOW23[is.na(LOW23)] <- 0

LOW23 <- as.data.frame(apply(LOW23,2,sum))
LOW23$SoCauTLSai <- as.numeric(row.names(LOW23))

LOW23$percent <- round(LOW23[,1]/sum(LOW23[,1]),3)
#graph----
GLOW23 <- ggplot(LOW23, aes(x = as.character(LOW23[,2]), y = LOW23[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW23[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 23", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))

#Learning outcome 31

LO31.last5.1922 <- table(apply(last5.1922[-1,last5.1922[1,]==31],1,sum))
LO31.last5.1923 <- table(apply(last5.1923[-1,last5.1923[1,]==31],1,sum))
LO31.last5.1924 <- table(apply(last5.1924[-1,last5.1924[1,]==31],1,sum))

rownames(LO31.last5.1922) <- abs(as.numeric(rownames(LO31.last5.1922)) - ncol(GKCDR[GKCDR[,1]==1922,GKCDR[2,] == 31]))
rownames(LO31.last5.1923) <- abs(as.numeric(rownames(LO31.last5.1923)) - ncol(GKCDR[GKCDR[,1]==1923,GKCDR[2,] == 31]))
rownames(LO31.last5.1924) <- abs(as.numeric(rownames(LO31.last5.1924)) - ncol(GKCDR[GKCDR[,1]==1924,GKCDR[2,] == 31]))


LO31.Diem1921.1921 <- table(apply(Diem1921.1921[-1,Diem1921.1921[1,]==31],1,sum))
LO31.Diem1921.1922 <- table(apply(Diem1921.1922[-1,Diem1921.1922[1,]==31],1,sum))
LO31.Diem1921.1923 <- table(apply(Diem1921.1923[-1,Diem1921.1923[1,]==31],1,sum))
LO31.Diem1921.1924 <- table(apply(Diem1921.1924[-1,Diem1921.1924[1,]==31],1,sum))
LO31.Diem1922.1921 <- table(apply(Diem1922.1921[-1,Diem1922.1921[1,]==31],1,sum))
LO31.Diem1922.1922 <- table(apply(Diem1922.1922[-1,Diem1922.1922[1,]==31],1,sum))
LO31.Diem1922.1923 <- table(apply(Diem1922.1923[-1,Diem1922.1923[1,]==31],1,sum))
LO31.Diem1922.1924 <- table(apply(Diem1922.1924[-1,Diem1922.1924[1,]==31],1,sum))
LO31.Diem1923.1921 <- table(apply(Diem1923.1921[-1,Diem1923.1921[1,]==31],1,sum))
LO31.Diem1923.1922 <- table(apply(Diem1923.1922[-1,Diem1923.1922[1,]==31],1,sum))
LO31.Diem1923.1923 <- table(apply(Diem1923.1923[-1,Diem1923.1923[1,]==31],1,sum))
LO31.Diem1923.1924 <- table(apply(Diem1923.1924[-1,Diem1923.1924[1,]==31],1,sum))
LO31.Diem1924.1921 <- table(apply(Diem1924.1921[-1,Diem1924.1921[1,]==31],1,sum))
LO31.Diem1924.1922 <- table(apply(Diem1924.1922[-1,Diem1924.1922[1,]==31],1,sum))
LO31.Diem1924.1923 <- table(apply(Diem1924.1923[-1,Diem1924.1923[1,]==31],1,sum))
LO31.Diem1924.1924 <- table(apply(Diem1924.1924[-1,Diem1924.1924[1,]==31],1,sum))

#make count right answer -> wrong answer
rownames(LO31.Diem1921.1921) <- abs(as.numeric(rownames(LO31.Diem1921.1921)) - ncol(MD1921.1921[,MD1921.1921[1,]==31]))
rownames(LO31.Diem1921.1922) <- abs(as.numeric(rownames(LO31.Diem1921.1922)) - ncol(MD1921.1922[,MD1921.1922[1,]==31]))
rownames(LO31.Diem1921.1923) <- abs(as.numeric(rownames(LO31.Diem1921.1923)) - ncol(MD1921.1923[,MD1921.1923[1,]==31]))
rownames(LO31.Diem1921.1924) <- abs(as.numeric(rownames(LO31.Diem1921.1924)) - ncol(MD1921.1924[,MD1921.1924[1,]==31]))

rownames(LO31.Diem1922.1921) <- abs(as.numeric(rownames(LO31.Diem1922.1921)) - ncol(MD1922.1921[,MD1922.1921[1,]==31]))
rownames(LO31.Diem1922.1922) <- abs(as.numeric(rownames(LO31.Diem1922.1922)) - ncol(MD1922.1922[,MD1922.1922[1,]==31]))
rownames(LO31.Diem1922.1923) <- abs(as.numeric(rownames(LO31.Diem1922.1923)) - ncol(MD1922.1923[,MD1922.1923[1,]==31]))
rownames(LO31.Diem1922.1924) <- abs(as.numeric(rownames(LO31.Diem1922.1924)) - ncol(MD1922.1924[,MD1922.1924[1,]==31]))


#-----------------

rownames(LO31.Diem1923.1921) <- abs(as.numeric(rownames(LO31.Diem1923.1921)) - ncol(MD1923.1921[,MD1923.1921[1,]==31]))
rownames(LO31.Diem1923.1922) <- abs(as.numeric(rownames(LO31.Diem1923.1922)) - ncol(MD1923.1922[,MD1923.1922[1,]==31]))
rownames(LO31.Diem1923.1923) <- abs(as.numeric(rownames(LO31.Diem1923.1923)) - ncol(MD1923.1923[,MD1923.1923[1,]==31]))
rownames(LO31.Diem1923.1924) <- abs(as.numeric(rownames(LO31.Diem1923.1924)) - ncol(MD1923.1924[,MD1923.1924[1,]==31]))
rownames(LO31.Diem1924.1921) <- abs(as.numeric(rownames(LO31.Diem1924.1921)) - ncol(MD1924.1921[,MD1924.1921[1,]==31]))
rownames(LO31.Diem1924.1922) <- abs(as.numeric(rownames(LO31.Diem1924.1922)) - ncol(MD1924.1922[,MD1924.1922[1,]==31]))
rownames(LO31.Diem1924.1923) <- abs(as.numeric(rownames(LO31.Diem1924.1923)) - ncol(MD1924.1923[,MD1924.1923[1,]==31]))
rownames(LO31.Diem1924.1924) <- abs(as.numeric(rownames(LO31.Diem1924.1924)) - ncol(MD1924.1924[,MD1924.1924[1,]==31]))




LOW31 <- as.data.frame(bind_rows(LO31.Diem1921.1921,
                                 LO31.Diem1921.1922,
                                 LO31.Diem1921.1923,
                                 LO31.Diem1921.1924,
                                 LO31.Diem1922.1921,
                                 LO31.Diem1922.1922,
                                 LO31.Diem1922.1923,
                                 LO31.Diem1922.1924,
                                 LO31.Diem1923.1921,
                                 LO31.Diem1923.1922,
                                 LO31.Diem1923.1923,
                                 LO31.Diem1923.1924,
                                 LO31.Diem1924.1921,
                                 LO31.Diem1924.1922,
                                 LO31.Diem1924.1923,
                                 LO31.Diem1924.1924,
                                 LO31.last5.1922,
                                 LO31.last5.1923,
                                 LO31.last5.1924
))

LOW31[is.na(LOW31)] <- 0

LOW31 <- as.data.frame(apply(LOW31,2,sum))
LOW31$SoCauTLSai <- as.numeric(row.names(LOW31))

LOW31$percent <- round(LOW31[,1]/sum(LOW31[,1]),3)


GLOW31 <- ggplot(LOW31, aes(x = as.character(LOW31[,2]), y = LOW31[,3])) + geom_bar(stat = "identity") + geom_text(aes(label=LOW31[,3]), vjust=-0.3, size=3.5, )+
  labs(title = "Learning Outcome 31", x = "So cau tra loi sai", y = "tan suat tuong duong") + theme(panel.background = element_rect(fill = "#d557d5"))
```

```{r}
#graph----
GW <- ggarrange(GLOW.GK11, GLOW12, GLOW.GK21, GLOW.GK22, GLOW23, GLOW31, GLOW.CK32, ncol=2, nrow = 4)
GW
```

Phan V
```{r}
# Cau VI.2
# Gan p = 1
p <- 1

#reuse DiemGK & DiemCK
DiemGKCK.2<- cbind( DiemGK[1:361,],DiemCK)

#add combined made
DiemGKCK.2$mdCK_GK <- as.numeric(paste(DiemGKCK.2[,29] ,DiemGKCK.2[,62], sep = ""))
#rearrange
DiemGKCK.2 <- DiemGKCK.2[,c(63,1:28, 33:61)]

colnames(DiemGKCK.2)[c(5:29)] <- c(1:25)
colnames(DiemGKCK.2)[c(30:58)] <- c(26:54)

Diem2.1921.1921 <- apply(bind_rows(MD1921.1921,DiemGKCK.2[DiemGKCK.2[1] == 19211921,c(2,5:58)]),2,as.numeric)
Diem2.1921.1922 <- apply(bind_rows(MD1921.1922,DiemGKCK.2[DiemGKCK.2[1] == 19211922,c(2,5:58)]),2,as.numeric)
Diem2.1921.1923 <- apply(bind_rows(MD1921.1923,DiemGKCK.2[DiemGKCK.2[1] == 19211923,c(2,5:58)]),2,as.numeric)
Diem2.1921.1924 <- apply(bind_rows(MD1921.1924,DiemGKCK.2[DiemGKCK.2[1] == 19211924,c(2,5:58)]),2,as.numeric)
Diem2.1922.1921 <- apply(bind_rows(MD1922.1921,DiemGKCK.2[DiemGKCK.2[1] == 19221921,c(2,5:58)]),2,as.numeric)
Diem2.1922.1922 <- apply(bind_rows(MD1922.1922,DiemGKCK.2[DiemGKCK.2[1] == 19221922,c(2,5:58)]),2,as.numeric)
Diem2.1922.1923 <- apply(bind_rows(MD1922.1923,DiemGKCK.2[DiemGKCK.2[1] == 19221923,c(2,5:58)]),2,as.numeric)
Diem2.1922.1924 <- apply(bind_rows(MD1922.1924,DiemGKCK.2[DiemGKCK.2[1] == 19221924,c(2,5:58)]),2,as.numeric)
Diem2.1923.1921 <- apply(bind_rows(MD1923.1921,DiemGKCK.2[DiemGKCK.2[1] == 19231921,c(2,5:58)]),2,as.numeric)
Diem2.1923.1922 <- apply(bind_rows(MD1923.1922,DiemGKCK.2[DiemGKCK.2[1] == 19231922,c(2,5:58)]),2,as.numeric)
Diem2.1923.1923 <- apply(bind_rows(MD1923.1923,DiemGKCK.2[DiemGKCK.2[1] == 19231923,c(2,5:58)]),2,as.numeric)
Diem2.1923.1924 <- apply(bind_rows(MD1923.1924,DiemGKCK.2[DiemGKCK.2[1] == 19231924,c(2,5:58)]),2,as.numeric)
Diem2.1924.1921 <- apply(bind_rows(MD1924.1921,DiemGKCK.2[DiemGKCK.2[1] == 19241921,c(2,5:58)]),2,as.numeric)
Diem2.1924.1922 <- apply(bind_rows(MD1924.1922,DiemGKCK.2[DiemGKCK.2[1] == 19241922,c(2,5:58)]),2,as.numeric)
Diem2.1924.1923 <- apply(bind_rows(MD1924.1923,DiemGKCK.2[DiemGKCK.2[1] == 19241923,c(2,5:58)]),2,as.numeric)
Diem2.1924.1924 <- apply(bind_rows(MD1924.1924,DiemGKCK.2[DiemGKCK.2[1] == 19241924,c(2,5:58)]),2,as.numeric)

Diem2.LO12.1921.1921 <- as.data.frame(Diem2.1921.1921[,Diem2.1921.1921[1,] == 12 | is.na(Diem2.1921.1921[1,])]) 
Diem2.LO12.1921.1922 <- as.data.frame(Diem2.1921.1922[,Diem2.1921.1922[1,] == 12 | is.na(Diem2.1921.1922[1,])]) 
Diem2.LO12.1921.1923 <- as.data.frame(Diem2.1921.1923[,Diem2.1921.1923[1,] == 12 | is.na(Diem2.1921.1923[1,])]) 
Diem2.LO12.1921.1924 <- as.data.frame(Diem2.1921.1924[,Diem2.1921.1924[1,] == 12 | is.na(Diem2.1921.1924[1,])]) 
Diem2.LO12.1922.1921 <- as.data.frame(Diem2.1922.1921[,Diem2.1922.1921[1,] == 12 | is.na(Diem2.1922.1921[1,])]) 
Diem2.LO12.1922.1922 <- as.data.frame(Diem2.1922.1922[,Diem2.1922.1922[1,] == 12 | is.na(Diem2.1922.1922[1,])]) 
Diem2.LO12.1922.1923 <- as.data.frame(Diem2.1922.1923[,Diem2.1922.1923[1,] == 12 | is.na(Diem2.1922.1923[1,])]) 
Diem2.LO12.1922.1924 <- as.data.frame(Diem2.1922.1924[,Diem2.1922.1924[1,] == 12 | is.na(Diem2.1922.1924[1,])]) 
Diem2.LO12.1923.1921 <- as.data.frame(Diem2.1923.1921[,Diem2.1923.1921[1,] == 12 | is.na(Diem2.1923.1921[1,])]) 
Diem2.LO12.1923.1922 <- as.data.frame(Diem2.1923.1922[,Diem2.1923.1922[1,] == 12 | is.na(Diem2.1923.1922[1,])]) 
Diem2.LO12.1923.1923 <- as.data.frame(Diem2.1923.1923[,Diem2.1923.1923[1,] == 12 | is.na(Diem2.1923.1923[1,])]) 
Diem2.LO12.1923.1924 <- as.data.frame(Diem2.1923.1924[,Diem2.1923.1924[1,] == 12 | is.na(Diem2.1923.1924[1,])]) 
Diem2.LO12.1924.1921 <- as.data.frame(Diem2.1924.1921[,Diem2.1924.1921[1,] == 12 | is.na(Diem2.1924.1921[1,])]) 
Diem2.LO12.1924.1922 <- as.data.frame(Diem2.1924.1922[,Diem2.1924.1922[1,] == 12 | is.na(Diem2.1924.1922[1,])]) 
Diem2.LO12.1924.1923 <- as.data.frame(Diem2.1924.1923[,Diem2.1924.1923[1,] == 12 | is.na(Diem2.1924.1923[1,])]) 
Diem2.LO12.1924.1924 <- as.data.frame(Diem2.1924.1924[,Diem2.1924.1924[1,] == 12 | is.na(Diem2.1924.1924[1,])]) 



Diem2.LO12.1921.1921$TLD<- apply(Diem2.LO12.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 12]))],1,sum)
Diem2.LO12.1921.1921[Diem2.LO12.1921.1921[-1,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 12]) - p,"NO"]

Diem2.LO12.1921.1921$TLD <- apply(Diem2.LO12.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 12]))],1,sum) 
Diem2.LO12.1921.1922$TLD <- apply(Diem2.LO12.1921.1922[,c(1:ncol(MD1921.1922[,MD1921.1922 == 12]))],1,sum) 
Diem2.LO12.1921.1923$TLD <- apply(Diem2.LO12.1921.1923[,c(1:ncol(MD1921.1923[,MD1921.1923 == 12]))],1,sum) 
Diem2.LO12.1921.1924$TLD <- apply(Diem2.LO12.1921.1924[,c(1:ncol(MD1921.1924[,MD1921.1924 == 12]))],1,sum) 
Diem2.LO12.1922.1921$TLD <- apply(Diem2.LO12.1922.1921[,c(1:ncol(MD1922.1921[,MD1922.1921 == 12]))],1,sum) 
Diem2.LO12.1922.1922$TLD <- apply(Diem2.LO12.1922.1922[,c(1:ncol(MD1922.1922[,MD1922.1922 == 12]))],1,sum) 
Diem2.LO12.1922.1923$TLD <- apply(Diem2.LO12.1922.1923[,c(1:ncol(MD1922.1923[,MD1922.1923 == 12]))],1,sum) 
Diem2.LO12.1922.1924$TLD <- apply(Diem2.LO12.1922.1924[,c(1:ncol(MD1922.1924[,MD1922.1924 == 12]))],1,sum) 
Diem2.LO12.1923.1921$TLD <- apply(Diem2.LO12.1923.1921[,c(1:ncol(MD1923.1921[,MD1923.1921 == 12]))],1,sum) 
Diem2.LO12.1923.1922$TLD <- apply(Diem2.LO12.1923.1922[,c(1:ncol(MD1923.1922[,MD1923.1922 == 12]))],1,sum) 
Diem2.LO12.1923.1923$TLD <- apply(Diem2.LO12.1923.1923[,c(1:ncol(MD1923.1923[,MD1923.1923 == 12]))],1,sum) 
Diem2.LO12.1923.1924$TLD <- apply(Diem2.LO12.1923.1924[,c(1:ncol(MD1923.1924[,MD1923.1924 == 12]))],1,sum) 
Diem2.LO12.1924.1921$TLD <- apply(Diem2.LO12.1924.1921[,c(1:ncol(MD1924.1921[,MD1924.1921 == 12]))],1,sum) 
Diem2.LO12.1924.1922$TLD <- apply(Diem2.LO12.1924.1922[,c(1:ncol(MD1924.1922[,MD1924.1922 == 12]))],1,sum) 
Diem2.LO12.1924.1923$TLD <- apply(Diem2.LO12.1924.1923[,c(1:ncol(MD1924.1923[,MD1924.1923 == 12]))],1,sum) 
Diem2.LO12.1924.1924$TLD <- apply(Diem2.LO12.1924.1924[,c(1:ncol(MD1924.1924[,MD1924.1924 == 12]))],1,sum) 


#list of student meets learning outcome 12 result requirement
LO12SVList <- ("SV")

LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1921[Diem2.LO12.1921.1921[,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1922[Diem2.LO12.1921.1922[,"TLD"] >= ncol(MD1921.1922[,MD1921.1922 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1923[Diem2.LO12.1921.1923[,"TLD"] >= ncol(MD1921.1923[,MD1921.1923 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1921.1924[Diem2.LO12.1921.1924[,"TLD"] >= ncol(MD1921.1924[,MD1921.1924 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1921[Diem2.LO12.1922.1921[,"TLD"] >= ncol(MD1922.1921[,MD1922.1921 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1922[Diem2.LO12.1922.1922[,"TLD"] >= ncol(MD1922.1922[,MD1922.1922 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1923[Diem2.LO12.1922.1923[,"TLD"] >= ncol(MD1922.1923[,MD1922.1923 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1922.1924[Diem2.LO12.1922.1924[,"TLD"] >= ncol(MD1922.1924[,MD1922.1924 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1921[Diem2.LO12.1923.1921[,"TLD"] >= ncol(MD1923.1921[,MD1923.1921 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1922[Diem2.LO12.1923.1922[,"TLD"] >= ncol(MD1923.1922[,MD1923.1922 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1923[Diem2.LO12.1923.1923[,"TLD"] >= ncol(MD1923.1923[,MD1923.1923 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1923.1924[Diem2.LO12.1923.1924[,"TLD"] >= ncol(MD1923.1924[,MD1923.1924 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1921[Diem2.LO12.1924.1921[,"TLD"] >= ncol(MD1924.1921[,MD1924.1921 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1922[Diem2.LO12.1924.1922[,"TLD"] >= ncol(MD1924.1922[,MD1924.1922 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1923[Diem2.LO12.1924.1923[,"TLD"] >= ncol(MD1924.1923[,MD1924.1923 == 12]) - p,"No" ])  
LO12SVList <- c(LO12SVList,Diem2.LO12.1924.1924[Diem2.LO12.1924.1924[,"TLD"] >= ncol(MD1924.1924[,MD1924.1924 == 12]) - p,"No" ])  




#list of student meets learning outcome 12 result requirement
LO12SVList
na.omit(as.data.frame(LO12SVList))


Diem2.LO31.1921.1921 <- as.data.frame(Diem2.1921.1921[,Diem2.1921.1921[1,] == 31 | is.na(Diem2.1921.1921[1,])]) 
Diem2.LO31.1921.1922 <- as.data.frame(Diem2.1921.1922[,Diem2.1921.1922[1,] == 31 | is.na(Diem2.1921.1922[1,])]) 
Diem2.LO31.1921.1923 <- as.data.frame(Diem2.1921.1923[,Diem2.1921.1923[1,] == 31 | is.na(Diem2.1921.1923[1,])]) 
Diem2.LO31.1921.1924 <- as.data.frame(Diem2.1921.1924[,Diem2.1921.1924[1,] == 31 | is.na(Diem2.1921.1924[1,])]) 
Diem2.LO31.1922.1921 <- as.data.frame(Diem2.1922.1921[,Diem2.1922.1921[1,] == 31 | is.na(Diem2.1922.1921[1,])]) 
Diem2.LO31.1922.1922 <- as.data.frame(Diem2.1922.1922[,Diem2.1922.1922[1,] == 31 | is.na(Diem2.1922.1922[1,])]) 
Diem2.LO31.1922.1923 <- as.data.frame(Diem2.1922.1923[,Diem2.1922.1923[1,] == 31 | is.na(Diem2.1922.1923[1,])]) 
Diem2.LO31.1922.1924 <- as.data.frame(Diem2.1922.1924[,Diem2.1922.1924[1,] == 31 | is.na(Diem2.1922.1924[1,])]) 
Diem2.LO31.1923.1921 <- as.data.frame(Diem2.1923.1921[,Diem2.1923.1921[1,] == 31 | is.na(Diem2.1923.1921[1,])]) 
Diem2.LO31.1923.1922 <- as.data.frame(Diem2.1923.1922[,Diem2.1923.1922[1,] == 31 | is.na(Diem2.1923.1922[1,])]) 
Diem2.LO31.1923.1923 <- as.data.frame(Diem2.1923.1923[,Diem2.1923.1923[1,] == 31 | is.na(Diem2.1923.1923[1,])]) 
Diem2.LO31.1923.1924 <- as.data.frame(Diem2.1923.1924[,Diem2.1923.1924[1,] == 31 | is.na(Diem2.1923.1924[1,])]) 
Diem2.LO31.1924.1921 <- as.data.frame(Diem2.1924.1921[,Diem2.1924.1921[1,] == 31 | is.na(Diem2.1924.1921[1,])]) 
Diem2.LO31.1924.1922 <- as.data.frame(Diem2.1924.1922[,Diem2.1924.1922[1,] == 31 | is.na(Diem2.1924.1922[1,])]) 
Diem2.LO31.1924.1923 <- as.data.frame(Diem2.1924.1923[,Diem2.1924.1923[1,] == 31 | is.na(Diem2.1924.1923[1,])]) 
Diem2.LO31.1924.1924 <- as.data.frame(Diem2.1924.1924[,Diem2.1924.1924[1,] == 31 | is.na(Diem2.1924.1924[1,])]) 



Diem2.LO31.1921.1921$TLD<- apply(Diem2.LO31.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 31]))],1,sum)
Diem2.LO31.1921.1921[Diem2.LO31.1921.1921[-1,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 31]) - p,"NO"]

Diem2.LO31.1921.1921$TLD <- apply(Diem2.LO31.1921.1921[,c(1:ncol(MD1921.1921[,MD1921.1921 == 31]))],1,sum) 
Diem2.LO31.1921.1922$TLD <- apply(Diem2.LO31.1921.1922[,c(1:ncol(MD1921.1922[,MD1921.1922 == 31]))],1,sum) 
Diem2.LO31.1921.1923$TLD <- apply(Diem2.LO31.1921.1923[,c(1:ncol(MD1921.1923[,MD1921.1923 == 31]))],1,sum) 
Diem2.LO31.1921.1924$TLD <- apply(Diem2.LO31.1921.1924[,c(1:ncol(MD1921.1924[,MD1921.1924 == 31]))],1,sum) 
Diem2.LO31.1922.1921$TLD <- apply(Diem2.LO31.1922.1921[,c(1:ncol(MD1922.1921[,MD1922.1921 == 31]))],1,sum) 
Diem2.LO31.1922.1922$TLD <- apply(Diem2.LO31.1922.1922[,c(1:ncol(MD1922.1922[,MD1922.1922 == 31]))],1,sum) 
Diem2.LO31.1922.1923$TLD <- apply(Diem2.LO31.1922.1923[,c(1:ncol(MD1922.1923[,MD1922.1923 == 31]))],1,sum) 
Diem2.LO31.1922.1924$TLD <- apply(Diem2.LO31.1922.1924[,c(1:ncol(MD1922.1924[,MD1922.1924 == 31]))],1,sum) 
Diem2.LO31.1923.1921$TLD <- apply(Diem2.LO31.1923.1921[,c(1:ncol(MD1923.1921[,MD1923.1921 == 31]))],1,sum) 
Diem2.LO31.1923.1922$TLD <- apply(Diem2.LO31.1923.1922[,c(1:ncol(MD1923.1922[,MD1923.1922 == 31]))],1,sum) 
Diem2.LO31.1923.1923$TLD <- apply(Diem2.LO31.1923.1923[,c(1:ncol(MD1923.1923[,MD1923.1923 == 31]))],1,sum) 
Diem2.LO31.1923.1924$TLD <- apply(Diem2.LO31.1923.1924[,c(1:ncol(MD1923.1924[,MD1923.1924 == 31]))],1,sum) 
Diem2.LO31.1924.1921$TLD <- apply(Diem2.LO31.1924.1921[,c(1:ncol(MD1924.1921[,MD1924.1921 == 31]))],1,sum) 
Diem2.LO31.1924.1922$TLD <- apply(Diem2.LO31.1924.1922[,c(1:ncol(MD1924.1922[,MD1924.1922 == 31]))],1,sum) 
Diem2.LO31.1924.1923$TLD <- apply(Diem2.LO31.1924.1923[,c(1:ncol(MD1924.1923[,MD1924.1923 == 31]))],1,sum) 
Diem2.LO31.1924.1924$TLD <- apply(Diem2.LO31.1924.1924[,c(1:ncol(MD1924.1924[,MD1924.1924 == 31]))],1,sum) 


#list of student meets learning outcome 31 result requirement
LO31SVList <- "SV"

LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1921[Diem2.LO31.1921.1921[,"TLD"] >= ncol(MD1921.1921[,MD1921.1921 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1922[Diem2.LO31.1921.1922[,"TLD"] >= ncol(MD1921.1922[,MD1921.1922 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1923[Diem2.LO31.1921.1923[,"TLD"] >= ncol(MD1921.1923[,MD1921.1923 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1921.1924[Diem2.LO31.1921.1924[,"TLD"] >= ncol(MD1921.1924[,MD1921.1924 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1921[Diem2.LO31.1922.1921[,"TLD"] >= ncol(MD1922.1921[,MD1922.1921 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1922[Diem2.LO31.1922.1922[,"TLD"] >= ncol(MD1922.1922[,MD1922.1922 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1923[Diem2.LO31.1922.1923[,"TLD"] >= ncol(MD1922.1923[,MD1922.1923 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1922.1924[Diem2.LO31.1922.1924[,"TLD"] >= ncol(MD1922.1924[,MD1922.1924 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1921[Diem2.LO31.1923.1921[,"TLD"] >= ncol(MD1923.1921[,MD1923.1921 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1922[Diem2.LO31.1923.1922[,"TLD"] >= ncol(MD1923.1922[,MD1923.1922 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1923[Diem2.LO31.1923.1923[,"TLD"] >= ncol(MD1923.1923[,MD1923.1923 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1923.1924[Diem2.LO31.1923.1924[,"TLD"] >= ncol(MD1923.1924[,MD1923.1924 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1921[Diem2.LO31.1924.1921[,"TLD"] >= ncol(MD1924.1921[,MD1924.1921 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1922[Diem2.LO31.1924.1922[,"TLD"] >= ncol(MD1924.1922[,MD1924.1922 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1923[Diem2.LO31.1924.1923[,"TLD"] >= ncol(MD1924.1923[,MD1924.1923 == 31]) - p,"No" ])  
LO31SVList <- c(LO31SVList,Diem2.LO31.1924.1924[Diem2.LO31.1924.1924[,"TLD"] >= ncol(MD1924.1924[,MD1924.1924 == 31]) - p,"No" ])  


#list of student meets learning outcome 31 result requirement
LO31SVList
na.omit(as.data.frame(LO31SVList))



#Learning outcome 23 handling exception data of midterm MD1922
#Learning outcome 23 only have 1 question in midterm (MD1922) and 2 in final
TotalQFrameGKEX
#majority of students have right answer for Lo23MD1922 midterm
DiemGK.1922.LO23
#No student have 3 right answer for total Learning outcome 23(mid/final).
LOW23
```

```{r}
# Cau VI.3
# IDK
# 
GKMN<- DiemGKCK.2[,c(3,5:29)]
Nhom1<-GKMN[GKMN[,1] == "L01",]
Nhom2<-GKMN[GKMN[,1] == "L02",]
Nhom3<-GKMN[GKMN[,1] == "L03",]
Nhom1$TongDiem <-round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1)
#average of each Nhom
mean(round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1))
mean(round(rowSums(apply(Nhom2[,c(2:26)],2,as.numeric))/25*10,1))
mean(round(rowSums(apply(Nhom3[,c(2:26)],2,as.numeric))/25*10,1))
#median of each Nhom
median(round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1))
median(round(rowSums(apply(Nhom2[,c(2:26)],2,as.numeric))/25*10,1))
median(round(rowSums(apply(Nhom3[,c(2:26)],2,as.numeric))/25*10,1))

#Frequency of each Nhom according to Diem

FNhom1 <-as.data.frame(table(round(rowSums(apply(Nhom1[,c(2:26)],2,as.numeric))/25*10,1)))
colnames(FNhom1) <- c("Diem", "Percentage")
FNhom1$Percentage <- round(FNhom1$Percentage/sum(FNhom1$Percentage)*100,2)

FNhom2 <-as.data.frame(table(round(rowSums(apply(Nhom2[,c(2:26)],2,as.numeric))/25*10,1)))
colnames(FNhom2) <- c("Diem", "Percentage")
FNhom2$Percentage <- round(FNhom2$Percentage/sum(FNhom2$Percentage)*100,2)

FNhom3 <-as.data.frame(table(round(rowSums(apply(Nhom3[,c(2:26)],2,as.numeric))/25*10,1)))
colnames(FNhom3) <- c("Diem", "Percentage")
FNhom3$Percentage <- round(FNhom3$Percentage/sum(FNhom3$Percentage)*100,2)

GNhom1 <- ggplot(FNhom1, aes(x = as.character(FNhom1[,1]), y = FNhom1[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=FNhom1[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Nhom 1", x = "Diem", y = "%SV cua nhom") + theme(panel.background = element_rect(fill = "#d557d5"))

GNhom2 <- ggplot(FNhom2, aes(x = as.character(FNhom2[,1]), y = FNhom2[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=FNhom2[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Nhom 2", x = "Diem", y = "%SV cua nhom") + theme(panel.background = element_rect(fill = "#d557d5"))

GNhom3 <- ggplot(FNhom3, aes(x = as.character(FNhom3[,1]), y = FNhom3[,2])) + geom_bar(stat = "identity") + geom_text(aes(label=FNhom3[,2]), vjust=-0.3, size=3.5, )+
  labs(title = "Nhom 3", x = "Diem", y = "%SV cua nhom") + theme(panel.background = element_rect(fill = "#d557d5"))

GNhom <- ggarrange(GNhom1,GNhom2,GNhom3, ncol = 3)

GNhom

#conclusion: 
#Nhom3 has high grade with highest average grade
#Nhom1 has low grade with lowest average grade
#Nhom2 has average grade slightly lower than Nhom3
```

